| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Robust | Adversarial Attacks on Graph Structured Data | Faizan [PDF + GaoJi Pdf | |
| Robust | KDD’18 Adversarial Attacks on Neural Networks for Graph Data | Faizan PDF + GaoJi Pdf | |
| Robust | Attacking Binarized Neural Networks | Faizan PDF |
Readings ByTag
Besides using high-level categories, we also use the following detailed tags to label each read post we finished. Click on a tag to see relevant list of readings.
adversarial-examples
adversarial-loss
alphago
amortized
analysis
architecture-search
associative
attention
attribution
autoencoder
autoregressive
auxiliary
backprop
beam
bert
bias
bias-variance
binarization
binary
black-box
blocking
brain
casual
certified-defense
chromatin
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composition
compression
concept
crispr
cryptography
curriculum
data-valuation
denoising
dialog
difference-analysis
differentiation
dimension-reduction
discrete
distillation
distributed
dna
domain-adaptation
dynamic
ehr
em
embedding
encoder-decoder
expressive
few-shot
forcing
forgetting
fuzzing
gan
gcn
gene-network
generalization
generative
genomics
geometric
graph
graph-attention
graphical-model
hash
heterogeneous
hierarchical
high-dimensional
hyperparameter
imitation-learning
imputation
influence-functions
infomax
informax
interpretable
invariant
knowledge-graph
language-processing
learn2learn
loss
low-rank
manifold
markov
matching
matching-net
matrix-completion
memorization
memory
meta-learning
metamorphic
metric-learning
mimic
mobile
model-as-sample
model-criticism
molecule
multi-label
multi-task
mutual-information
neural-programming
neuroscience
nlp
noise
nonparametric
normalization
ntm
optimization
parallel
parsimonious
planning
pointer
privacy
program
propagation
protein
pruning
qa
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regularization
relational
rl
rna
rnn
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sample-selection
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scalable
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set
shapley
sketch
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software-testing
sparsity
structured
stylometric
submodular
subspace
temporal-difference
text
training
transfer
transfer-learning
trees
tutorial
understanding
vae
value-networks
variational
verification
visualizing
white-box
[1]: adversarial-examples
Table of readings
read on: - 06 Mar 2019
read on: - 02 Dec 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Jennifer | Adversarial Attacks Against Medical Deep Learning Systems | ||
| Jennifer | Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learning | ||
| Jennifer | Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers | ||
| Jennifer | CleverHans | ||
| Ji | Ji-f18-New papers about adversarial attack |
read on: - 20 Nov 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Bill | Adversarial Examples that Fool both Computer Vision and Time-Limited Humans | ||
| Bill | Adversarial Attacks Against Medical Deep Learning Systems | ||
| Bill | TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing | ||
| Bill | Distilling the Knowledge in a Neural Network | ||
| Bill | Defensive Distillation is Not Robust to Adversarial Examples | ||
| Bill | Adversarial Logit Pairing , Harini Kannan, Alexey Kurakin, Ian Goodfellow |
read on: - 03 Aug 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| GaoJi | Deep Reinforcement Fuzzing, Konstantin Böttinger, Patrice Godefroid, Rishabh Singh | ||
| GaoJi | Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks, Guy Katz, Clark Barrett, David Dill, Kyle Julian, Mykel Kochenderfer | ||
| GaoJi | DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars, Yuchi Tian, Kexin Pei, Suman Jana, Baishakhi Ray | ||
| GaoJi | A few Recent (2018) papers on Black-box Adversarial Attacks, like Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors 1 | ||
| GaoJi | A few Recent papers of Adversarial Attacks on reinforcement learning, like Adversarial Attacks on Neural Network Policies (Sandy Huang, Nicolas Papernot, Ian Goodfellow, Yan Duan, Pieter Abbeel) | ||
| Testing | DeepXplore: Automated Whitebox Testing of Deep Learning Systems |
read on: - 20 May 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Bill | Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples | ||
| Bill | Adversarial Examples for Evaluating Reading Comprehension Systems, Robin Jia, Percy Liang | ||
| Bill | Certified Defenses against Adversarial Examples, Aditi Raghunathan, Jacob Steinhardt, Percy Liang | ||
| Bill | Provably Minimally-Distorted Adversarial Examples, Nicholas Carlini, Guy Katz, Clark Barrett, David L. Dill |
read on: - 12 May 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Bill | Intriguing Properties of Adversarial Examples, Ekin D. Cubuk, Barret Zoph, Samuel S. Schoenholz, Quoc V. Le 1 | ||
| Bill | Adversarial Spheres 2 | ||
| Bill | Adversarial Transformation Networks: Learning to Generate Adversarial Examples, Shumeet Baluja, Ian Fischer 3 | ||
| Bill | Thermometer encoding: one hot way to resist adversarial examples 4 | ||
| Adversarial Logit Pairing , Harini Kannan, Alexey Kurakin, Ian Goodfellow 5 |
read on: - 26 Oct 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Tianlu | Robustness of classifiers: from adversarial to random noise, NIPS16 | PDF 1 | |
| Anant | Blind Attacks on Machine Learners, 2 NIPS16 | ||
| Data Noising as Smoothing in Neural Network Language Models (Ng), ICLR17 3 | |||
| The Robustness of Estimator Composition, NIPS16 4 |
read on: - 23 Oct 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| GaoJi | Delving into Transferable Adversarial Examples and Black-box Attacks,ICLR17 1 | ||
| Shijia | On Detecting Adversarial Perturbations, ICLR17 2 | ||
| Anant | Parseval Networks: Improving Robustness to Adversarial Examples, ICML17 3 | ||
| Bargav | Being Robust (in High Dimensions) Can Be Practical, ICML17 4 |
read on: - 19 Jan 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| AE | Intriguing properties of neural networks / | ||
| AE | Explaining and Harnessing Adversarial Examples | ||
| AE | Towards Deep Learning Models Resistant to Adversarial Attacks | ||
| AE | DeepFool: a simple and accurate method to fool deep neural networks | ||
| AE | Towards Evaluating the Robustness of Neural Networks by Carlini and Wagner | ||
| Data | Basic Survey of ImageNet - LSVRC competition | URL | |
| Understand | Understanding Black-box Predictions via Influence Functions | ||
| Understand | Deep inside convolutional networks: Visualising image classification models and saliency maps | ||
| Understand | BeenKim, Interpretable Machine Learning, ICML17 Tutorial [^1] | ||
| provable | Provable defenses against adversarial examples via the convex outer adversarial polytope, Eric Wong, J. Zico Kolter, | URL |
Table of readings
read on: - 05 Jul 2020
| Index | Papers | Our Slides |
|---|---|---|
| 1 | BIAS ALSO MATTERS: BIAS ATTRIBUTION FOR DEEP NEURAL NETWORK EXPLANATION | Arsh Survey |
| 2 | Data Shapley: Equitable Valuation of Data for Machine Learning | Arsh Survey |
| What is your data worth? Equitable Valuation of Data | Sanchit Survey | |
| 3 | Neural Network Attributions: A Causal Perspective | Zhe Survey |
| 4 | Defending Against Neural Fake News | Eli Survey |
| 5 | Interpretation of Neural Networks is Fragile | Eli Survey |
| Interpretation of Neural Networks is Fragile | Pan Survey | |
| 6 | Parsimonious Black-Box Adversarial Attacks Via Efficient Combinatorial Optimization | Eli Survey |
| 7 | Retrofitting Word Vectors to Semantic Lexicons | Morris Survey |
| 8 | On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models | Morris Survey |
| 9 | Towards Deep Learning Models Resistant to Adversarial Attacks | Pan Survey |
| 10 | Robust Attribution Regularization | Pan Survey |
| 11 | Sanity Checks for Saliency Maps | Sanchit Survey |
| 12 | Survey of data generation and evaluation in Interpreting DNN pipelines | Sanchit Survey |
| 13 | Think Architecture First: Benchmarking Deep Learning Interpretability in Time Series Predictions | Sanchit Survey |
| 14 | Universal Adversarial Triggers for Attacking and Analyzing NLP | Sanchit Survey |
| 15 | Apricot: Submodular selection for data summarization in Python | Arsh Survey |
read on: - 10 Dec 2019
| Team INDEX | Title & Link | Tags | Our Slide |
|---|---|---|---|
| T3 | Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints | submodular, coreset, safety | OurSlide |
| T6 | Decision Boundary Analysis of Adversarial Examples | adversarial-examples | OurSlide |
| T8 | Robustness may be at odds with accuracy | robustness | OurSlide |
| T18 | Towards Reverse-Engineering Black-Box Neural Networks | meta, model-as-sample, safety, privacy | OurSlide |
| T23 | The Odds are Odd: A Statistical Test for Detecting Adversarial Examples | adversarial-examples | OurSlide |
| T25 | Learning how to explain neural networks: PatternNet and PatternAttribution | Attribution, Interpretable | OurSlide |
| T31 | Detecting Statistical Interactions from Neural Network Weights | Interpretable, Relational | OurSlide |
[2]: adversarial-loss
Table of readings
read on: - 11 May 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Chao | Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification | ||
| Jack | FastXML: A Fast, Accurate and Stable Tree-classifier for eXtreme Multi-label Learning | ||
| BasicMLC | Multi-Label Classification: An Overview | ||
| SPEN | Structured Prediction Energy Networks | ||
| InfNet | Learning Approximate Inference Networks for Structured Prediction | ||
| SPENMLC | Deep Value Networks | ||
| Adversarial | Semantic Segmentation using Adversarial Networks | ||
| EmbedMLC | StarSpace: Embed All The Things! | ||
| deepMLC | CNN-RNN: A Unified Framework for Multi-label Image Classification/ CVPR 2016 | ||
| deepMLC | Order-Free RNN with Visual Attention for Multi-Label Classification / AAAI 2018 |
[3]: alphago
Table of readings
read on: - 28 Nov 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Anant | The Predictron: End-to-End Learning and Planning, ICLR17 1 | ||
| ChaoJiang | Szepesvari - Theory of RL 2 | RLSS.pdf + Video | |
| GaoJi | Mastering the game of Go without human knowledge / Nature 2017 3 | ||
| Thomas - Safe Reinforcement Learning | RLSS17.pdf + video | ||
| Sutton - Temporal-Difference Learning | RLSS17.pdf + Video |
[4]: amortized
Table of readings
read on: - 23 Aug 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables, Chris J. Maddison, Andriy Mnih, Yee Whye Teh 1 | ||
| GaoJi | Summary Of Several Autoencoder models | ||
| GaoJi | Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models, Jesse Engel, Matthew Hoffman, Adam Roberts 2 | ||
| GaoJi | Summary of A Few Recent Papers about Discrete Generative models, SeqGAN, MaskGAN, BEGAN, BoundaryGAN | ||
| Arshdeep | Semi-Amortized Variational Autoencoders, Yoon Kim, Sam Wiseman, Andrew C. Miller, David Sontag, Alexander M. Rush 3 | ||
| Arshdeep | Synthesizing Programs for Images using Reinforced Adversarial Learning, Yaroslav Ganin, Tejas Kulkarni, Igor Babuschkin, S.M. Ali Eslami, Oriol Vinyals 4 |
[5]: analysis
Table of readings
read on: - 12 Dec 2019
| Team INDEX | Title & Link | Tags | Our Slide |
|---|---|---|---|
| T2 | Empirical Study of Example Forgetting During Deep Neural Network Learning | Sample Selection, forgetting | OurSlide |
| T29 | Select Via Proxy: Efficient Data Selection For Training Deep Networks | Sample Selection | OurSlide |
| T9 | How SGD Selects the Global Minima in over-parameterized Learning | optimization | OurSlide |
| T10 | Escaping Saddles with Stochastic Gradients | optimization | OurSlide |
| T13 | To What Extent Do Different Neural Networks Learn the Same Representation | subspace | OurSlide |
| T19 | On the Information Bottleneck Theory of Deep Learning | informax | OurSlide |
| T20 | Visualizing the Loss Landscape of Neural Nets | normalization | OurSlide |
| T21 | Using Pre-Training Can Improve Model Robustness and Uncertainty | training, analysis | OurSlide |
| T24 | Norm matters: efficient and accurate normalization schemes in deep networks | normalization | OurSlide |
[6]: architecture-search
Table of readings
read on: - 02 Nov 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| GaoJi | Neural Architecture Search with Reinforcement Learning, ICLR17 1 | ||
| Ceyer | Learning to learn 2 | DLSS17video | |
| Beilun | Optimization as a Model for Few-Shot Learning, ICLR17 3 | PDF + More | |
| Anant | Neural Optimizer Search with Reinforcement Learning, ICML17 4 |
read on: - 05 Oct 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Anant | AdaNet: Adaptive Structural Learning of Artificial Neural Networks, ICML17 1 | ||
| Shijia | SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization, ICML17 2 | ||
| Jack | Proximal Deep Structured Models, NIPS16 3 | ||
| Optimal Architectures in a Solvable Model of Deep Networks, NIPS16 4 | |||
| Tianlu | Large-Scale Evolution of Image Classifiers, ICML17 5 |
Table of readings
read on: - 25 Oct 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | Learning Transferable Architectures for Scalable Image Recognition | ||
| Arshdeep | FractalNet: Ultra-Deep Neural Networks without Residuals |
read on: - 07 Nov 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| GaoJi | Forward and Reverse Gradient-Based Hyperparameter Optimization, ICML17 1 | ||
| Chaojiang | Adaptive Neural Networks for Efficient Inference, ICML17 2 | ||
| Bargav | Practical Gauss-Newton Optimisation for Deep Learning, ICML17 3 | ||
| Rita | How to Escape Saddle Points Efficiently, ICML17 4 | ||
| Batched High-dimensional Bayesian Optimization via Structural Kernel Learning |
[7]: associative
Table of readings
[8]: attention
Table of readings
read on: - 05 Aug 2020
| Index | Papers | Our Slides |
|---|---|---|
| 0 | A survey on Interpreting Deep Learning Models | Eli Survey |
| Interpretable Machine Learning: Definitions,Methods, Applications | Arsh Survey | |
| 1 | Explaining Explanations: Axiomatic Feature Interactions for Deep Networks | Arsh Survey |
| 2 | Shapley Value review | Arsh Survey |
| L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data | Bill Survey | |
| Consistent Individualized Feature Attribution for Tree Ensembles | bill Survey | |
| Summary for A value for n-person games | Pan Survey | |
| L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data | Rishab Survey | |
| 3 | Hierarchical Interpretations of Neural Network Predictions | Arsh Survey |
| Hierarchical Interpretations of Neural Network Predictions | Rishab Survey | |
| 4 | Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs | Arsh Survey |
| Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs | Rishab Survey | |
| 5 | Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Models | Rishab Survey |
| Sanchit Survey | ||
| Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection | Sanchit Survey | |
| 6 | This Looks Like That: Deep Learning for Interpretable Image Recognition | Pan Survey |
| 7 | AllenNLP Interpret | Rishab Survey |
| 8 | DISCOVERY OF NATURAL LANGUAGE CONCEPTS IN INDIVIDUAL UNITS OF CNNs | Rishab Survey |
| 9 | How Does BERT Answer Questions? A Layer-Wise Analysis of Transformer Representations | Rishab Survey |
| 10 | Attention is not Explanation | Sanchit Survey |
| Pan Survey | ||
| 11 | Axiomatic Attribution for Deep Networks | Sanchit Survey |
| 12 | Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization | Sanchit Survey |
| 13 | Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifier | Sanchit Survey |
| 14 | “Why Should I Trust You?”Explaining the Predictions of Any Classifier | Yu Survey |
| 15 | INTERPRETATIONS ARE USEFUL: PENALIZING EXPLANATIONS TO ALIGN NEURAL NETWORKS WITH PRIOR KNOWLEDGE | Pan Survey |
read on: - 05 Apr 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Understand | Faithful and Customizable Explanations of Black Box Models | Derrick PDF | |
| Understand | A causal framework for explaining the predictions of black-box sequence-to-sequence models, EMNLP17 | GaoJi PDF + Bill Pdf | |
| Understand | How Powerful are Graph Neural Networks? / Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning | Pdf + Pdf | GaoJi PDF |
| Understand | Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs + GNN Explainer: A Tool for Post-hoc Explanation of Graph Neural Networks | Pdf + PDF | GaoJi PDF |
| Understand | Attention is not Explanation, 2019 | ||
| Understand | Understanding attention in graph neural networks, 2019 |
read on: - 15 Feb 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Bio | KDEEP: Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks, 2018 1 | Eli Pdf | |
| Bio | Molecular geometry prediction using a deep generative graph neural network | Eli Pdf | |
| Bio | Visualizing convolutional neural network protein-ligand scoring | PDF() | Eli PDF |
| Bio | Deep generative models of genetic variation capture mutation effects | PDF() | Eli PDF |
| Bio | Attentive cross-modal paratope prediction | Eli PDF |
read on: - 11 May 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Chao | Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification | ||
| Jack | FastXML: A Fast, Accurate and Stable Tree-classifier for eXtreme Multi-label Learning | ||
| BasicMLC | Multi-Label Classification: An Overview | ||
| SPEN | Structured Prediction Energy Networks | ||
| InfNet | Learning Approximate Inference Networks for Structured Prediction | ||
| SPENMLC | Deep Value Networks | ||
| Adversarial | Semantic Segmentation using Adversarial Networks | ||
| EmbedMLC | StarSpace: Embed All The Things! | ||
| deepMLC | CNN-RNN: A Unified Framework for Multi-label Image Classification/ CVPR 2016 | ||
| deepMLC | Order-Free RNN with Visual Attention for Multi-Label Classification / AAAI 2018 |
read on: - 03 May 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | Show, Attend and Tell: Neural Image Caption Generation with Visual Attention 1 | ||
| Arshdeep | Latent Alignment and Variational Attention 2 | ||
| Arshdeep | Modularity Matters: Learning Invariant Relational Reasoning Tasks, Jason Jo, Vikas Verma, Yoshua Bengio 3 |
read on: - 14 Nov 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| ChaoJiang | Courville - Generative Models II | DLSS17Slide + video | |
| GaoJi | Attend, Infer, Repeat: Fast Scene Understanding with Generative Models, NIPS16 1 | PDF + talk | |
| Arshdeep | Composing graphical models with neural networks for structured representations and fast inference, NIPS16 2 | ||
| Johnson - Graphical Models and Deep Learning | DLSSSlide + video | ||
| Parallel Multiscale Autoregressive Density Estimation, ICML17 3 | |||
| Beilun | Conditional Image Generation with Pixel CNN Decoders, NIPS16 4 | ||
| Shijia | Marrying Graphical Models & Deep Learning | DLSS17 + Video |
read on: - 28 Sep 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Jack | Attend, Adapt and Transfer: Attentive Deep Architecture for Adaptive Transfer from multiple sources in the same domain, ICLR17 1 | ||
| Arshdeep | Bidirectional Attention Flow for Machine Comprehension, ICLR17 2 | PDF + code | |
| Ceyer | Image-to-Markup Generation with Coarse-to-Fine Attention, ICML17 | PDF + code | |
| ChaoJiang | Can Active Memory Replace Attention? ; Samy Bengio, NIPS16 3 | ||
| An Information-Theoretic Framework for Fast and Robust Unsupervised Learning via Neural Population Infomax, ICLR17 |
read on: - 26 Sep 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Rita | Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer, ICLR17 1 | ||
| Tianlu | Dynamic Coattention Networks For Question Answering, ICLR17 2 | PDF + code | |
| ChaoJiang | Structured Attention Networks, ICLR17 3 | PDF + code |
read on: - 18 Jan 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| seq2seq | Sequence to Sequence Learning with Neural Networks | ||
| Set | Pointer Networks | ||
| Set | Order Matters: Sequence to Sequence for Sets | ||
| Point Attention | Multiple Object Recognition with Visual Attention | ||
| Memory | End-To-End Memory Networks | Jack Survey | |
| Memory | Neural Turing Machines | ||
| Memory | Hybrid computing using a neural network with dynamic external memory | ||
| Muthu | Matching Networks for One Shot Learning (NIPS16) 1 | ||
| Jack | Meta-Learning with Memory-Augmented Neural Networks (ICML16) 2 | ||
| Metric | ICML07 Best Paper - Information-Theoretic Metric Learning |
read on: - 12 Jan 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| NLP | A Neural Probabilistic Language Model | ||
| Text | Bag of Tricks for Efficient Text Classification | ||
| Text | Character-level Convolutional Networks for Text Classification | ||
| NLP | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | ||
| seq2seq | Neural Machine Translation by Jointly Learning to Align and Translate | ||
| NLP | Natural Language Processing (almost) from Scratch | ||
| Train | Curriculum learning | ||
| Muthu | NeuroIPS Embedding Papers survey 2012 to 2015 | NIPS | |
| Basics | Efficient BackProp |
[9]: attribution
Table of readings
read on: - 10 Dec 2019
| Team INDEX | Title & Link | Tags | Our Slide |
|---|---|---|---|
| T3 | Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints | submodular, coreset, safety | OurSlide |
| T6 | Decision Boundary Analysis of Adversarial Examples | adversarial-examples | OurSlide |
| T8 | Robustness may be at odds with accuracy | robustness | OurSlide |
| T18 | Towards Reverse-Engineering Black-Box Neural Networks | meta, model-as-sample, safety, privacy | OurSlide |
| T23 | The Odds are Odd: A Statistical Test for Detecting Adversarial Examples | adversarial-examples | OurSlide |
| T25 | Learning how to explain neural networks: PatternNet and PatternAttribution | Attribution, Interpretable | OurSlide |
| T31 | Detecting Statistical Interactions from Neural Network Weights | Interpretable, Relational | OurSlide |
read on: - 12 Oct 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Jack | A Unified Approach to Interpreting Model Predictions | ||
| Jack | “Why Should I Trust You?”: Explaining the Predictions of Any Classifier | ||
| Jack | Visual Feature Attribution using Wasserstein GANs | ||
| Jack | GAN Dissection: Visualizing and Understanding Generative Adversarial Networks | ||
| GaoJi | Recent Interpretable machine learning papers | ||
| Jennifer | The Building Blocks of Interpretability |
read on: - 11 Oct 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Rita | Visualizing Deep Neural Network Decisions: Prediction Difference Analysis, ICLR17 1 | ||
| Arshdeep | Axiomatic Attribution for Deep Networks, ICML17 2 | ||
| The Robustness of Estimator Composition, NIPS16 |
read on: - 10 Oct 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Rita | Learning Important Features Through Propagating Activation Differences, ICML17 1 | ||
| GaoJi | Examples are not Enough, Learn to Criticize! Model Criticism for Interpretable Machine Learning, NIPS16 2 | ||
| Rita | Learning Kernels with Random Features, Aman Sinha*; John Duchi, 3 |
[10]: autoencoder
Table of readings
read on: - 15 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Generate | Maximum-Likelihood Augmented Discrete Generative Adversarial Networks | Tkach PDF + GaoJi Pdf | |
| Generate | Graphical Generative Adversarial Networks | Arshdeep PDF | |
| Generate | GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models, ICML2018 | Arshdeep PDF | |
| Generate | Inference in probabilistic graphical models by Graph Neural Networks | Arshdeep PDF | |
| Generate | Encoding robust representation for graph generation | Arshdeep PDF | |
| Generate | Junction Tree Variational Autoencoder for Molecular Graph Generation | Tkach PDF + Arshdeep Pdf | |
| Generate | Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation NeurIPS18 | Tkach PDF | |
| Generate | Towards Variational Generation of Small Graphs | Tkach PDF + Arshdeep Pdf | |
| Generate | Convolutional Imputation of Matrix Networks | Tkach PDF | |
| Generate | Graph Convolutional Matrix Completion | Tkach PDF | |
| Generate | NetGAN: Generating Graphs via Random Walks ICML18 | [ULR] | Tkach PDF |
| Beam | Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement | URL | Tkach PDF |
read on: - 29 Dec 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Tkach | Boundary-Seeking Generative Adversarial Networks | ||
| Tkach | Maximum-Likelihood Augmented Discrete Generative Adversarial Networks | ||
| Tkach | Generating Sentences from a Continuous Space |
read on: - 21 Dec 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | Constrained Graph Variational Autoencoders for Molecule Design | ||
| Arshdeep | Learning Deep Generative Models of Graphs | ||
| Arshdeep | Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation | ||
| Jack | Generating and designing DNA with deep generative models |
read on: - 23 Aug 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables, Chris J. Maddison, Andriy Mnih, Yee Whye Teh 1 | ||
| GaoJi | Summary Of Several Autoencoder models | ||
| GaoJi | Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models, Jesse Engel, Matthew Hoffman, Adam Roberts 2 | ||
| GaoJi | Summary of A Few Recent Papers about Discrete Generative models, SeqGAN, MaskGAN, BEGAN, BoundaryGAN | ||
| Arshdeep | Semi-Amortized Variational Autoencoders, Yoon Kim, Sam Wiseman, Andrew C. Miller, David Sontag, Alexander M. Rush 3 | ||
| Arshdeep | Synthesizing Programs for Images using Reinforced Adversarial Learning, Yaroslav Ganin, Tejas Kulkarni, Igor Babuschkin, S.M. Ali Eslami, Oriol Vinyals 4 |
[11]: autoregressive
Table of readings
read on: - 14 Nov 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| ChaoJiang | Courville - Generative Models II | DLSS17Slide + video | |
| GaoJi | Attend, Infer, Repeat: Fast Scene Understanding with Generative Models, NIPS16 1 | PDF + talk | |
| Arshdeep | Composing graphical models with neural networks for structured representations and fast inference, NIPS16 2 | ||
| Johnson - Graphical Models and Deep Learning | DLSSSlide + video | ||
| Parallel Multiscale Autoregressive Density Estimation, ICML17 3 | |||
| Beilun | Conditional Image Generation with Pixel CNN Decoders, NIPS16 4 | ||
| Shijia | Marrying Graphical Models & Deep Learning | DLSS17 + Video |
[12]: auxiliary
Table of readings
read on: - 30 Nov 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Ceyer | Reinforcement Learning with Unsupervised Auxiliary Tasks, ICLR17 1 | ||
| Beilun | Why is Posterior Sampling Better than Optimism for Reinforcement Learning? Ian Osband, Benjamin Van Roy 2 | ||
| Ji | Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction, ICML17 3 | ||
| Xueying | End-to-End Differentiable Adversarial Imitation Learning, ICML17 4 | ||
| Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs, ICML17 | |||
| FeUdal Networks for Hierarchical Reinforcement Learning, ICML17 5 |
[13]: backprop
Table of readings
read on: - 12 Jan 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| NLP | A Neural Probabilistic Language Model | ||
| Text | Bag of Tricks for Efficient Text Classification | ||
| Text | Character-level Convolutional Networks for Text Classification | ||
| NLP | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | ||
| seq2seq | Neural Machine Translation by Jointly Learning to Align and Translate | ||
| NLP | Natural Language Processing (almost) from Scratch | ||
| Train | Curriculum learning | ||
| Muthu | NeuroIPS Embedding Papers survey 2012 to 2015 | NIPS | |
| Basics | Efficient BackProp |
[14]: beam
Table of readings
read on: - 15 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Generate | Maximum-Likelihood Augmented Discrete Generative Adversarial Networks | Tkach PDF + GaoJi Pdf | |
| Generate | Graphical Generative Adversarial Networks | Arshdeep PDF | |
| Generate | GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models, ICML2018 | Arshdeep PDF | |
| Generate | Inference in probabilistic graphical models by Graph Neural Networks | Arshdeep PDF | |
| Generate | Encoding robust representation for graph generation | Arshdeep PDF | |
| Generate | Junction Tree Variational Autoencoder for Molecular Graph Generation | Tkach PDF + Arshdeep Pdf | |
| Generate | Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation NeurIPS18 | Tkach PDF | |
| Generate | Towards Variational Generation of Small Graphs | Tkach PDF + Arshdeep Pdf | |
| Generate | Convolutional Imputation of Matrix Networks | Tkach PDF | |
| Generate | Graph Convolutional Matrix Completion | Tkach PDF | |
| Generate | NetGAN: Generating Graphs via Random Walks ICML18 | [ULR] | Tkach PDF |
| Beam | Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement | URL | Tkach PDF |
[15]: bert
Table of readings
read on: - 07 Dec 2019
| Team INDEX | Title & Link | Tags | Our Slide |
|---|---|---|---|
| T11 | Parameter-Efficient Transfer Learning for NLP | meta, BERT, text, Transfer | OurSlide |
| T22 | Deep Asymmetric Multi-task Feature Learning | meta, regularization, Multi-task | OurSlide |
read on: - 12 Jan 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| NLP | A Neural Probabilistic Language Model | ||
| Text | Bag of Tricks for Efficient Text Classification | ||
| Text | Character-level Convolutional Networks for Text Classification | ||
| NLP | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | ||
| seq2seq | Neural Machine Translation by Jointly Learning to Align and Translate | ||
| NLP | Natural Language Processing (almost) from Scratch | ||
| Train | Curriculum learning | ||
| Muthu | NeuroIPS Embedding Papers survey 2012 to 2015 | NIPS | |
| Basics | Efficient BackProp |
[16]: bias
Table of readings
read on: - 05 Jul 2020
| Index | Papers | Our Slides |
|---|---|---|
| 1 | BIAS ALSO MATTERS: BIAS ATTRIBUTION FOR DEEP NEURAL NETWORK EXPLANATION | Arsh Survey |
| 2 | Data Shapley: Equitable Valuation of Data for Machine Learning | Arsh Survey |
| What is your data worth? Equitable Valuation of Data | Sanchit Survey | |
| 3 | Neural Network Attributions: A Causal Perspective | Zhe Survey |
| 4 | Defending Against Neural Fake News | Eli Survey |
| 5 | Interpretation of Neural Networks is Fragile | Eli Survey |
| Interpretation of Neural Networks is Fragile | Pan Survey | |
| 6 | Parsimonious Black-Box Adversarial Attacks Via Efficient Combinatorial Optimization | Eli Survey |
| 7 | Retrofitting Word Vectors to Semantic Lexicons | Morris Survey |
| 8 | On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models | Morris Survey |
| 9 | Towards Deep Learning Models Resistant to Adversarial Attacks | Pan Survey |
| 10 | Robust Attribution Regularization | Pan Survey |
| 11 | Sanity Checks for Saliency Maps | Sanchit Survey |
| 12 | Survey of data generation and evaluation in Interpreting DNN pipelines | Sanchit Survey |
| 13 | Think Architecture First: Benchmarking Deep Learning Interpretability in Time Series Predictions | Sanchit Survey |
| 14 | Universal Adversarial Triggers for Attacking and Analyzing NLP | Sanchit Survey |
| 15 | Apricot: Submodular selection for data summarization in Python | Arsh Survey |
[17]: bias-variance
Table of readings
[18]: binarization
Table of readings
read on: - 06 Dec 2019
| Team INDEX | Title & Link | Tags | Our Slide |
|---|---|---|---|
| T33 | The High-Dimensional Geometry of Binary Neural Networks | Quantization, binarization, scalable | OurSlide |
| T34 | Modern Neural Networks Generalize on Small Data Sets | small-data, analysis, ensemble | OurSlide |
| T4 | Cognitive Scheduler for Heterogeneous High Performance Computing System | system-application | OurSlide |
[19]: binary
Table of readings
read on: - 25 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Edge | MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications | ||
| Edge | XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks | URL | Ryan PDF |
| Edge | DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices | Eamon PDF | |
| Edge | Loss-aware Binarization of Deep Networks, ICLR17 | Ryan PDF | |
| Edge | Espresso: Efficient Forward Propagation for Binary Deep Neural Networks | Eamon PDF | |
| Dynamic | Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution | Weilin PDF | |
| Dynamic | Dynamic Scheduling For Dynamic Control Flow in Deep Learning Systems | ||
| Dynamic | Cavs: An Efficient Runtime System for Dynamic Neural Networks |
read on: - 06 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Robust | Adversarial Attacks on Graph Structured Data | Faizan [PDF + GaoJi Pdf | |
| Robust | KDD’18 Adversarial Attacks on Neural Networks for Graph Data | Faizan PDF + GaoJi Pdf | |
| Robust | Attacking Binarized Neural Networks | Faizan PDF |
read on: - 22 Jun 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction 1 | ||
| Arshdeep | Decoupled Neural Interfaces Using Synthetic Gradients 2 | ||
| Arshdeep | Diet Networks: Thin Parameters for Fat Genomics 3 | ||
| Arshdeep | Metric Learning with Adaptive Density Discrimination 4 |
read on: - 02 Jun 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | HyperNetworks, David Ha, Andrew Dai, Quoc V. Le ICLR 2017 1 | ||
| Arshdeep | Learning feed-forward one-shot learners 2 | ||
| Arshdeep | Learning to Learn by gradient descent by gradient descent 3 | ||
| Arshdeep | Dynamic Filter Networks 4 https://arxiv.org/abs/1605.09673 |
read on: - 21 Jan 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| DeepBind | Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning | ||
| DeepSEA | Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk | ||
| DeepSEA | Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction, ICML 2014 | ||
| BioBasics | A method for integrating and ranking the evidence for biochemical pathways by mining reactions from text, Bioinformatics13 | ||
| BioBasics | Efficient counting of k-mers in DNA sequences using a Bloom filter. Melsted P, Pritchard JK. BMC Bioinformatics. 2011 | ||
| BioBasics | Fast String Kernels using Inexact Matching for Protein Sequence, JMLR 2004 | ||
| BioBasics | NIPS09: Locality-Sensitive Binary Codes from Shift-Invariant Kernels | ||
| MedSignal | Segmenting Time Series: A Survey and Novel Approach, |
read on: - 20 Jan 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| scalable | Sanjiv Kumar (Columbia EECS 6898), Lecture: Introduction to large-scale machine learning 2010 [^1] | ||
| data scalable | Alex Smola - Berkeley SML: Scalable Machine Learning: Syllabus 2012 [^2] | PDF 2014 + PDF | |
| Binary | Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 | ||
| Model | Binary embeddings with structured hashed projections 1 | ||
| Model | Deep Compression: Compressing Deep Neural Networks (ICLR 2016) 2 |
[20]: black-box
Table of readings
read on: - 05 Aug 2020
| Index | Papers | Our Slides |
|---|---|---|
| 0 | A survey on Interpreting Deep Learning Models | Eli Survey |
| Interpretable Machine Learning: Definitions,Methods, Applications | Arsh Survey | |
| 1 | Explaining Explanations: Axiomatic Feature Interactions for Deep Networks | Arsh Survey |
| 2 | Shapley Value review | Arsh Survey |
| L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data | Bill Survey | |
| Consistent Individualized Feature Attribution for Tree Ensembles | bill Survey | |
| Summary for A value for n-person games | Pan Survey | |
| L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data | Rishab Survey | |
| 3 | Hierarchical Interpretations of Neural Network Predictions | Arsh Survey |
| Hierarchical Interpretations of Neural Network Predictions | Rishab Survey | |
| 4 | Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs | Arsh Survey |
| Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs | Rishab Survey | |
| 5 | Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Models | Rishab Survey |
| Sanchit Survey | ||
| Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection | Sanchit Survey | |
| 6 | This Looks Like That: Deep Learning for Interpretable Image Recognition | Pan Survey |
| 7 | AllenNLP Interpret | Rishab Survey |
| 8 | DISCOVERY OF NATURAL LANGUAGE CONCEPTS IN INDIVIDUAL UNITS OF CNNs | Rishab Survey |
| 9 | How Does BERT Answer Questions? A Layer-Wise Analysis of Transformer Representations | Rishab Survey |
| 10 | Attention is not Explanation | Sanchit Survey |
| Pan Survey | ||
| 11 | Axiomatic Attribution for Deep Networks | Sanchit Survey |
| 12 | Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization | Sanchit Survey |
| 13 | Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifier | Sanchit Survey |
| 14 | “Why Should I Trust You?”Explaining the Predictions of Any Classifier | Yu Survey |
| 15 | INTERPRETATIONS ARE USEFUL: PENALIZING EXPLANATIONS TO ALIGN NEURAL NETWORKS WITH PRIOR KNOWLEDGE | Pan Survey |
read on: - 05 Apr 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Understand | Faithful and Customizable Explanations of Black Box Models | Derrick PDF | |
| Understand | A causal framework for explaining the predictions of black-box sequence-to-sequence models, EMNLP17 | GaoJi PDF + Bill Pdf | |
| Understand | How Powerful are Graph Neural Networks? / Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning | Pdf + Pdf | GaoJi PDF |
| Understand | Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs + GNN Explainer: A Tool for Post-hoc Explanation of Graph Neural Networks | Pdf + PDF | GaoJi PDF |
| Understand | Attention is not Explanation, 2019 | ||
| Understand | Understanding attention in graph neural networks, 2019 |
read on: - 03 Aug 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| GaoJi | Deep Reinforcement Fuzzing, Konstantin Böttinger, Patrice Godefroid, Rishabh Singh | ||
| GaoJi | Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks, Guy Katz, Clark Barrett, David Dill, Kyle Julian, Mykel Kochenderfer | ||
| GaoJi | DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars, Yuchi Tian, Kexin Pei, Suman Jana, Baishakhi Ray | ||
| GaoJi | A few Recent (2018) papers on Black-box Adversarial Attacks, like Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors 1 | ||
| GaoJi | A few Recent papers of Adversarial Attacks on reinforcement learning, like Adversarial Attacks on Neural Network Policies (Sandy Huang, Nicolas Papernot, Ian Goodfellow, Yan Duan, Pieter Abbeel) | ||
| Testing | DeepXplore: Automated Whitebox Testing of Deep Learning Systems |
read on: - 14 Sep 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| SE | Equivariance Through Parameter-Sharing, ICML17 1 | ||
| SE | Why Deep Neural Networks for Function Approximation?, ICLR17 2 | ||
| SE | Geometry of Neural Network Loss Surfaces via Random Matrix Theory, 3ICML17 | ||
| Sharp Minima Can Generalize For Deep Nets, ICML17 4 |
read on: - 12 Sep 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Ceyer | A Closer Look at Memorization in Deep Networks, ICML17 1 | ||
| On the Expressive Efficiency of Overlapping Architectures of Deep Learning 2 | DLSSpdf + video | ||
| Mutual Information | Opening the Black Box of Deep Neural Networks via Information 3 | URL + video | |
| ChaoJiang | Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity, NIPS16 |
read on: - 07 Sep 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Beilun | Learning Deep Parsimonious Representations, NIPS16 1 | ||
| Jack | Dense Associative Memory for Pattern Recognition, NIPS16 2 | PDF + video |
read on: - 05 Sep 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Rita | On the Expressive Power of Deep Neural Networks 1 | ||
| Arshdeep | Understanding deep learning requires rethinking generalization, ICLR17 2 | ||
| Tianlu | On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima, ICLR17 3 |
read on: - 22 Jul 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| GaoJi | A few useful things to know about machine learning | ||
| GaoJi | A few papers related to testing learning, e.g., Understanding Black-box Predictions via Influence Functions | ||
| GaoJi | Automated White-box Testing of Deep Learning Systems 1 | ||
| GaoJi | Testing and Validating Machine Learning Classifiers by Metamorphic Testing 2 | ||
| GaoJi | Software testing: a research travelogue (2000–2014) |
[21]: blocking
Table of readings
read on: - 21 Sep 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Shijia | Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer, (Dean), ICLR17 1 | ||
| Ceyer | Sequence Modeling via Segmentations, ICML17 2 | ||
| Arshdeep | Input Switched Affine Networks: An RNN Architecture Designed for Interpretability, ICML17 3 |
[22]: brain
Table of readings
read on: - 13 Oct 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | deepCRISPR: optimized CRISPR guide RNA design by deep learning , Genome Biology 2018 | ||
| Arshdeep | The CRISPR tool kit for genome editing and beyond, Mazhar Adli | ||
| Eric | Intro of Genetic Engineering | ||
| Eric | Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs | ||
| Brandon | Generative Modeling for Protein Structure | URL |
read on: - 13 Aug 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. | ||
| Arshdeep | Solving the RNA design problem with reinforcement learning, PLOSCB 1 | ||
| Arshdeep | Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk 2 | ||
| Arshdeep | Towards Gene Expression Convolutions using Gene Interaction Graphs, Francis Dutil, Joseph Paul Cohen, Martin Weiss, Georgy Derevyanko, Yoshua Bengio 3 | ||
| Brandon | Kipoi: Accelerating the Community Exchange and Reuse of Predictive Models for Genomics | ||
| Arshdeep | Feedback GAN (FBGAN) for DNA: a Novel Feedback-Loop Architecture for Optimizing Protein Functions 2 |
[23]: casual
Table of readings
read on: - 05 Jan 2020
| Index | Papers | Our Slides |
|---|---|---|
| 1 | A Flexible Generative Framework for Graph-based Semi-supervised Learning | Arsh Survey |
| 2 | Learning Discrete Structures for Graph Neural Networks | Arsh Survey |
| 4 | Graph Markov Neural Nets | Arsh Survey |
| Graph Markov Neural Networks | Jack Survey | |
| 5 | GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations | Arsh Survey |
| 6 | Subgraph Neural Networks | Arsh Survey |
| 7 | Pointer Graph Networks | Arsh Survey |
| 8 | Modeling Relational Data with Graph Convolutional Networks | Arsh Survey |
| 9 | Graph Learning | Zhe Survey |
| 8 | Neural Relational Inference | Zhe Survey |
Table of readings
read on: - 05 Aug 2020
| Index | Papers | Our Slides |
|---|---|---|
| 0 | A survey on Interpreting Deep Learning Models | Eli Survey |
| Interpretable Machine Learning: Definitions,Methods, Applications | Arsh Survey | |
| 1 | Explaining Explanations: Axiomatic Feature Interactions for Deep Networks | Arsh Survey |
| 2 | Shapley Value review | Arsh Survey |
| L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data | Bill Survey | |
| Consistent Individualized Feature Attribution for Tree Ensembles | bill Survey | |
| Summary for A value for n-person games | Pan Survey | |
| L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data | Rishab Survey | |
| 3 | Hierarchical Interpretations of Neural Network Predictions | Arsh Survey |
| Hierarchical Interpretations of Neural Network Predictions | Rishab Survey | |
| 4 | Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs | Arsh Survey |
| Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs | Rishab Survey | |
| 5 | Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Models | Rishab Survey |
| Sanchit Survey | ||
| Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection | Sanchit Survey | |
| 6 | This Looks Like That: Deep Learning for Interpretable Image Recognition | Pan Survey |
| 7 | AllenNLP Interpret | Rishab Survey |
| 8 | DISCOVERY OF NATURAL LANGUAGE CONCEPTS IN INDIVIDUAL UNITS OF CNNs | Rishab Survey |
| 9 | How Does BERT Answer Questions? A Layer-Wise Analysis of Transformer Representations | Rishab Survey |
| 10 | Attention is not Explanation | Sanchit Survey |
| Pan Survey | ||
| 11 | Axiomatic Attribution for Deep Networks | Sanchit Survey |
| 12 | Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization | Sanchit Survey |
| 13 | Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifier | Sanchit Survey |
| 14 | “Why Should I Trust You?”Explaining the Predictions of Any Classifier | Yu Survey |
| 15 | INTERPRETATIONS ARE USEFUL: PENALIZING EXPLANATIONS TO ALIGN NEURAL NETWORKS WITH PRIOR KNOWLEDGE | Pan Survey |
read on: - 05 Apr 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Understand | Faithful and Customizable Explanations of Black Box Models | Derrick PDF | |
| Understand | A causal framework for explaining the predictions of black-box sequence-to-sequence models, EMNLP17 | GaoJi PDF + Bill Pdf | |
| Understand | How Powerful are Graph Neural Networks? / Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning | Pdf + Pdf | GaoJi PDF |
| Understand | Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs + GNN Explainer: A Tool for Post-hoc Explanation of Graph Neural Networks | Pdf + PDF | GaoJi PDF |
| Understand | Attention is not Explanation, 2019 | ||
| Understand | Understanding attention in graph neural networks, 2019 |
[24]: certified-defense
Table of readings
read on: - 20 May 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Bill | Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples | ||
| Bill | Adversarial Examples for Evaluating Reading Comprehension Systems, Robin Jia, Percy Liang | ||
| Bill | Certified Defenses against Adversarial Examples, Aditi Raghunathan, Jacob Steinhardt, Percy Liang | ||
| Bill | Provably Minimally-Distorted Adversarial Examples, Nicholas Carlini, Guy Katz, Clark Barrett, David L. Dill |
read on: - 19 Jan 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| AE | Intriguing properties of neural networks / | ||
| AE | Explaining and Harnessing Adversarial Examples | ||
| AE | Towards Deep Learning Models Resistant to Adversarial Attacks | ||
| AE | DeepFool: a simple and accurate method to fool deep neural networks | ||
| AE | Towards Evaluating the Robustness of Neural Networks by Carlini and Wagner | ||
| Data | Basic Survey of ImageNet - LSVRC competition | URL | |
| Understand | Understanding Black-box Predictions via Influence Functions | ||
| Understand | Deep inside convolutional networks: Visualising image classification models and saliency maps | ||
| Understand | BeenKim, Interpretable Machine Learning, ICML17 Tutorial [^1] | ||
| provable | Provable defenses against adversarial examples via the convex outer adversarial polytope, Eric Wong, J. Zico Kolter, | URL |
[25]: chromatin
Table of readings
read on: - 05 Jun 2020
| Index | Papers | Our Slides |
|---|---|---|
| 1 | Protein 3D Structure Computed from Evolutionary Sequence Variation | Arsh Survey |
| 3 | Regulatory network inference on developmental and evolutionary lineages | Arsh Survey |
| 4 | Deep learning in ultrasound image analysis | Zhe Survey |
| 5 | Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning (DeepBind) | Jack Survey |
| 6 | Canonical and single-cell Hi-C reveal distinct chromatin interaction sub-networks of mammalian transcription factors | Jack Survey |
| 7 | BindSpace decodes transcription factor binding signals by large-scale sequence embedding | Jack Survey |
| 8 | FastXML: A Fast, Accurate and Stable Tree-classifier for eXtreme Multi-label Learning | Jack Survey |
| 9 | Query-Reduction Networks for Question Answering | Bill Survey |
[26]: cnn
Table of readings
read on: - 11 Dec 2019
| Team INDEX | Title & Link | Tags | Our Slide | |
|---|---|---|---|---|
| T5 | Deep Structured Prediction with Nonlinear Output Transformations | structured | OurSlide | |
| T12 | Large Margin Deep Networks for Classification | OurSlide | large-margin | |
| T15 | Wide Activation for Efficient and Accurate Image Super-Resolution | CNN | OurSlide | |
| T17 | Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks | RNN | OurSlide | |
| T28 | Processing of missing data by neural networks | imputation | OurSlide | |
| T27 | Implicit Acceleration by Overparameterization | analysis | OurSlide |
[27]: composition
Table of readings
read on: - 14 Nov 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| ChaoJiang | Courville - Generative Models II | DLSS17Slide + video | |
| GaoJi | Attend, Infer, Repeat: Fast Scene Understanding with Generative Models, NIPS16 1 | PDF + talk | |
| Arshdeep | Composing graphical models with neural networks for structured representations and fast inference, NIPS16 2 | ||
| Johnson - Graphical Models and Deep Learning | DLSSSlide + video | ||
| Parallel Multiscale Autoregressive Density Estimation, ICML17 3 | |||
| Beilun | Conditional Image Generation with Pixel CNN Decoders, NIPS16 4 | ||
| Shijia | Marrying Graphical Models & Deep Learning | DLSS17 + Video |
read on: - 26 Oct 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Tianlu | Robustness of classifiers: from adversarial to random noise, NIPS16 | PDF 1 | |
| Anant | Blind Attacks on Machine Learners, 2 NIPS16 | ||
| Data Noising as Smoothing in Neural Network Language Models (Ng), ICLR17 3 | |||
| The Robustness of Estimator Composition, NIPS16 4 |
[28]: compression
Table of readings
read on: - 20 Jan 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| scalable | Sanjiv Kumar (Columbia EECS 6898), Lecture: Introduction to large-scale machine learning 2010 [^1] | ||
| data scalable | Alex Smola - Berkeley SML: Scalable Machine Learning: Syllabus 2012 [^2] | PDF 2014 + PDF | |
| Binary | Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 | ||
| Model | Binary embeddings with structured hashed projections 1 | ||
| Model | Deep Compression: Compressing Deep Neural Networks (ICLR 2016) 2 |
[29]: concept
Table of readings
read on: - 05 Aug 2020
| Index | Papers | Our Slides |
|---|---|---|
| 0 | A survey on Interpreting Deep Learning Models | Eli Survey |
| Interpretable Machine Learning: Definitions,Methods, Applications | Arsh Survey | |
| 1 | Explaining Explanations: Axiomatic Feature Interactions for Deep Networks | Arsh Survey |
| 2 | Shapley Value review | Arsh Survey |
| L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data | Bill Survey | |
| Consistent Individualized Feature Attribution for Tree Ensembles | bill Survey | |
| Summary for A value for n-person games | Pan Survey | |
| L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data | Rishab Survey | |
| 3 | Hierarchical Interpretations of Neural Network Predictions | Arsh Survey |
| Hierarchical Interpretations of Neural Network Predictions | Rishab Survey | |
| 4 | Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs | Arsh Survey |
| Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs | Rishab Survey | |
| 5 | Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Models | Rishab Survey |
| Sanchit Survey | ||
| Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection | Sanchit Survey | |
| 6 | This Looks Like That: Deep Learning for Interpretable Image Recognition | Pan Survey |
| 7 | AllenNLP Interpret | Rishab Survey |
| 8 | DISCOVERY OF NATURAL LANGUAGE CONCEPTS IN INDIVIDUAL UNITS OF CNNs | Rishab Survey |
| 9 | How Does BERT Answer Questions? A Layer-Wise Analysis of Transformer Representations | Rishab Survey |
| 10 | Attention is not Explanation | Sanchit Survey |
| Pan Survey | ||
| 11 | Axiomatic Attribution for Deep Networks | Sanchit Survey |
| 12 | Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization | Sanchit Survey |
| 13 | Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifier | Sanchit Survey |
| 14 | “Why Should I Trust You?”Explaining the Predictions of Any Classifier | Yu Survey |
| 15 | INTERPRETATIONS ARE USEFUL: PENALIZING EXPLANATIONS TO ALIGN NEURAL NETWORKS WITH PRIOR KNOWLEDGE | Pan Survey |
[30]: crispr
Table of readings
read on: - 13 Oct 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | deepCRISPR: optimized CRISPR guide RNA design by deep learning , Genome Biology 2018 | ||
| Arshdeep | The CRISPR tool kit for genome editing and beyond, Mazhar Adli | ||
| Eric | Intro of Genetic Engineering | ||
| Eric | Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs | ||
| Brandon | Generative Modeling for Protein Structure | URL |
[31]: cryptography
Table of readings
read on: - 22 Feb 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Tobin | Summary of A few Papers on: Machine Learning and Cryptography, (e.g., learning to Protect Communications with Adversarial Neural Cryptography) 1 | ||
| Tobin | Privacy Aware Learning (NIPS12) 2 | ||
| Tobin | Can Machine Learning be Secure?(2006) |
[32]: curriculum
Table of readings
read on: - 31 Oct 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Ceyer | An overview of gradient optimization algorithms, 1 | ||
| Shijia | Osborne - Probabilistic numerics for deep learning 2 | DLSS 2017 + Video | PDF / PDF2 |
| Jack | Automated Curriculum Learning for Neural Networks, ICML17 3 | ||
| DLSS17 | Johnson - Automatic Differentiation 4 | slide + video |
Table of readings
read on: - 12 Jan 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| NLP | A Neural Probabilistic Language Model | ||
| Text | Bag of Tricks for Efficient Text Classification | ||
| Text | Character-level Convolutional Networks for Text Classification | ||
| NLP | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | ||
| seq2seq | Neural Machine Translation by Jointly Learning to Align and Translate | ||
| NLP | Natural Language Processing (almost) from Scratch | ||
| Train | Curriculum learning | ||
| Muthu | NeuroIPS Embedding Papers survey 2012 to 2015 | NIPS | |
| Basics | Efficient BackProp |
[33]: data-valuation
Table of readings
read on: - 05 Jul 2020
| Index | Papers | Our Slides |
|---|---|---|
| 1 | BIAS ALSO MATTERS: BIAS ATTRIBUTION FOR DEEP NEURAL NETWORK EXPLANATION | Arsh Survey |
| 2 | Data Shapley: Equitable Valuation of Data for Machine Learning | Arsh Survey |
| What is your data worth? Equitable Valuation of Data | Sanchit Survey | |
| 3 | Neural Network Attributions: A Causal Perspective | Zhe Survey |
| 4 | Defending Against Neural Fake News | Eli Survey |
| 5 | Interpretation of Neural Networks is Fragile | Eli Survey |
| Interpretation of Neural Networks is Fragile | Pan Survey | |
| 6 | Parsimonious Black-Box Adversarial Attacks Via Efficient Combinatorial Optimization | Eli Survey |
| 7 | Retrofitting Word Vectors to Semantic Lexicons | Morris Survey |
| 8 | On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models | Morris Survey |
| 9 | Towards Deep Learning Models Resistant to Adversarial Attacks | Pan Survey |
| 10 | Robust Attribution Regularization | Pan Survey |
| 11 | Sanity Checks for Saliency Maps | Sanchit Survey |
| 12 | Survey of data generation and evaluation in Interpreting DNN pipelines | Sanchit Survey |
| 13 | Think Architecture First: Benchmarking Deep Learning Interpretability in Time Series Predictions | Sanchit Survey |
| 14 | Universal Adversarial Triggers for Attacking and Analyzing NLP | Sanchit Survey |
| 15 | Apricot: Submodular selection for data summarization in Python | Arsh Survey |
[34]: denoising
Table of readings
read on: - 16 Nov 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | Generalization and Equilibrium in Generative Adversarial Nets (ICML17) 1 | PDF + video | |
| Arshdeep | Mode Regularized Generative Adversarial Networks (ICLR17) 2 | ||
| Bargav | Improving Generative Adversarial Networks with Denoising Feature Matching, ICLR17 3 | ||
| Anant | Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy, ICLR17 4 | PDF + code |
[35]: dialog
Table of readings
read on: - 19 Sep 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Jack | Learning End-to-End Goal-Oriented Dialog, ICLR17 1 | ||
| Bargav | Nonparametric Neural Networks, ICLR17 2 | ||
| Bargav | Learning Structured Sparsity in Deep Neural Networks, NIPS16 3 | ||
| Arshdeep | Learning the Number of Neurons in Deep Networks, NIPS16 4 |
[36]: difference-analysis
Table of readings
read on: - 11 Oct 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Rita | Visualizing Deep Neural Network Decisions: Prediction Difference Analysis, ICLR17 1 | ||
| Arshdeep | Axiomatic Attribution for Deep Networks, ICML17 2 | ||
| The Robustness of Estimator Composition, NIPS16 |
read on: - 10 Oct 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Rita | Learning Important Features Through Propagating Activation Differences, ICML17 1 | ||
| GaoJi | Examples are not Enough, Learn to Criticize! Model Criticism for Interpretable Machine Learning, NIPS16 2 | ||
| Rita | Learning Kernels with Random Features, Aman Sinha*; John Duchi, 3 |
[37]: differentiation
Table of readings
read on: - 31 Oct 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Ceyer | An overview of gradient optimization algorithms, 1 | ||
| Shijia | Osborne - Probabilistic numerics for deep learning 2 | DLSS 2017 + Video | PDF / PDF2 |
| Jack | Automated Curriculum Learning for Neural Networks, ICML17 3 | ||
| DLSS17 | Johnson - Automatic Differentiation 4 | slide + video |
[38]: dimension-reduction
Table of readings
read on: - 20 Jan 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| scalable | Sanjiv Kumar (Columbia EECS 6898), Lecture: Introduction to large-scale machine learning 2010 [^1] | ||
| data scalable | Alex Smola - Berkeley SML: Scalable Machine Learning: Syllabus 2012 [^2] | PDF 2014 + PDF | |
| Binary | Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 | ||
| Model | Binary embeddings with structured hashed projections 1 | ||
| Model | Deep Compression: Compressing Deep Neural Networks (ICLR 2016) 2 |
[39]: discrete
Table of readings
read on: - 22 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Scalable | FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling | Ryan PDF + Arshdeep Pdf | |
| Scalable | MILE: A Multi-Level Framework for Scalable Graph Embedding | Ryan PDF | |
| Scalable | LanczosNet: Multi-Scale Deep Graph Convolutional Networks | Ryan PDF | |
| Scalable | Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis | Derrick PDF | |
| Scalable | Towards Federated learning at Scale: System Design | URL | Derrick PDF |
| Scalable | DNN Dataflow Choice Is Overrated | Derrick PDF | |
| Scalable | Towards Efficient Large-Scale Graph Neural Network Computing | Derrick PDF | |
| Scalable | PyTorch Geometric | URL | |
| Scalable | PyTorch BigGraph | URL | |
| Scalable | Simplifying Graph Convolutional Networks | ||
| Scalable | Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks |
read on: - 15 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Generate | Maximum-Likelihood Augmented Discrete Generative Adversarial Networks | Tkach PDF + GaoJi Pdf | |
| Generate | Graphical Generative Adversarial Networks | Arshdeep PDF | |
| Generate | GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models, ICML2018 | Arshdeep PDF | |
| Generate | Inference in probabilistic graphical models by Graph Neural Networks | Arshdeep PDF | |
| Generate | Encoding robust representation for graph generation | Arshdeep PDF | |
| Generate | Junction Tree Variational Autoencoder for Molecular Graph Generation | Tkach PDF + Arshdeep Pdf | |
| Generate | Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation NeurIPS18 | Tkach PDF | |
| Generate | Towards Variational Generation of Small Graphs | Tkach PDF + Arshdeep Pdf | |
| Generate | Convolutional Imputation of Matrix Networks | Tkach PDF | |
| Generate | Graph Convolutional Matrix Completion | Tkach PDF | |
| Generate | NetGAN: Generating Graphs via Random Walks ICML18 | [ULR] | Tkach PDF |
| Beam | Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement | URL | Tkach PDF |
read on: - 29 Dec 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Tkach | Boundary-Seeking Generative Adversarial Networks | ||
| Tkach | Maximum-Likelihood Augmented Discrete Generative Adversarial Networks | ||
| Tkach | Generating Sentences from a Continuous Space |
read on: - 21 Dec 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | Constrained Graph Variational Autoencoders for Molecule Design | ||
| Arshdeep | Learning Deep Generative Models of Graphs | ||
| Arshdeep | Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation | ||
| Jack | Generating and designing DNA with deep generative models |
read on: - 23 Aug 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables, Chris J. Maddison, Andriy Mnih, Yee Whye Teh 1 | ||
| GaoJi | Summary Of Several Autoencoder models | ||
| GaoJi | Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models, Jesse Engel, Matthew Hoffman, Adam Roberts 2 | ||
| GaoJi | Summary of A Few Recent Papers about Discrete Generative models, SeqGAN, MaskGAN, BEGAN, BoundaryGAN | ||
| Arshdeep | Semi-Amortized Variational Autoencoders, Yoon Kim, Sam Wiseman, Andrew C. Miller, David Sontag, Alexander M. Rush 3 | ||
| Arshdeep | Synthesizing Programs for Images using Reinforced Adversarial Learning, Yaroslav Ganin, Tejas Kulkarni, Igor Babuschkin, S.M. Ali Eslami, Oriol Vinyals 4 |
[40]: distillation
Table of readings
read on: - 20 Nov 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Bill | Adversarial Examples that Fool both Computer Vision and Time-Limited Humans | ||
| Bill | Adversarial Attacks Against Medical Deep Learning Systems | ||
| Bill | TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing | ||
| Bill | Distilling the Knowledge in a Neural Network | ||
| Bill | Defensive Distillation is Not Robust to Adversarial Examples | ||
| Bill | Adversarial Logit Pairing , Harini Kannan, Alexey Kurakin, Ian Goodfellow |
[41]: distributed
Table of readings
read on: - 22 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Scalable | FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling | Ryan PDF + Arshdeep Pdf | |
| Scalable | MILE: A Multi-Level Framework for Scalable Graph Embedding | Ryan PDF | |
| Scalable | LanczosNet: Multi-Scale Deep Graph Convolutional Networks | Ryan PDF | |
| Scalable | Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis | Derrick PDF | |
| Scalable | Towards Federated learning at Scale: System Design | URL | Derrick PDF |
| Scalable | DNN Dataflow Choice Is Overrated | Derrick PDF | |
| Scalable | Towards Efficient Large-Scale Graph Neural Network Computing | Derrick PDF | |
| Scalable | PyTorch Geometric | URL | |
| Scalable | PyTorch BigGraph | URL | |
| Scalable | Simplifying Graph Convolutional Networks | ||
| Scalable | Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks |
Table of readings
read on: - 20 Jan 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| scalable | Sanjiv Kumar (Columbia EECS 6898), Lecture: Introduction to large-scale machine learning 2010 [^1] | ||
| data scalable | Alex Smola - Berkeley SML: Scalable Machine Learning: Syllabus 2012 [^2] | PDF 2014 + PDF | |
| Binary | Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 | ||
| Model | Binary embeddings with structured hashed projections 1 | ||
| Model | Deep Compression: Compressing Deep Neural Networks (ICLR 2016) 2 |
[42]: dna
Table of readings
read on: - 15 Feb 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Bio | KDEEP: Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks, 2018 1 | Eli Pdf | |
| Bio | Molecular geometry prediction using a deep generative graph neural network | Eli Pdf | |
| Bio | Visualizing convolutional neural network protein-ligand scoring | PDF() | Eli PDF |
| Bio | Deep generative models of genetic variation capture mutation effects | PDF() | Eli PDF |
| Bio | Attentive cross-modal paratope prediction | Eli PDF |
read on: - 21 Dec 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | Constrained Graph Variational Autoencoders for Molecule Design | ||
| Arshdeep | Learning Deep Generative Models of Graphs | ||
| Arshdeep | Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation | ||
| Jack | Generating and designing DNA with deep generative models |
read on: - 13 Oct 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | deepCRISPR: optimized CRISPR guide RNA design by deep learning , Genome Biology 2018 | ||
| Arshdeep | The CRISPR tool kit for genome editing and beyond, Mazhar Adli | ||
| Eric | Intro of Genetic Engineering | ||
| Eric | Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs | ||
| Brandon | Generative Modeling for Protein Structure | URL |
read on: - 13 Aug 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. | ||
| Arshdeep | Solving the RNA design problem with reinforcement learning, PLOSCB 1 | ||
| Arshdeep | Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk 2 | ||
| Arshdeep | Towards Gene Expression Convolutions using Gene Interaction Graphs, Francis Dutil, Joseph Paul Cohen, Martin Weiss, Georgy Derevyanko, Yoshua Bengio 3 | ||
| Brandon | Kipoi: Accelerating the Community Exchange and Reuse of Predictive Models for Genomics | ||
| Arshdeep | Feedback GAN (FBGAN) for DNA: a Novel Feedback-Loop Architecture for Optimizing Protein Functions 2 |
read on: - 20 Apr 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| BrandonLiu | Summary of Recent Generative Adversarial Networks (Classified) | ||
| Jack | Generating and designing DNA with deep generative models, Nathan Killoran, Leo J. Lee, Andrew Delong, David Duvenaud, Brendan J. Frey | ||
| GaoJi | More about basics of GAN | ||
| McGan: Mean and Covariance Feature Matching GAN, PMLR 70:2527-2535 | |||
| Wasserstein GAN, ICML17 | |||
| Geometrical Insights for Implicit Generative Modeling, L Bottou, M Arjovsky, D Lopez-Paz, M Oquab |
read on: - 21 Jan 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| DeepBind | Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning | ||
| DeepSEA | Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk | ||
| DeepSEA | Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction, ICML 2014 | ||
| BioBasics | A method for integrating and ranking the evidence for biochemical pathways by mining reactions from text, Bioinformatics13 | ||
| BioBasics | Efficient counting of k-mers in DNA sequences using a Bloom filter. Melsted P, Pritchard JK. BMC Bioinformatics. 2011 | ||
| BioBasics | Fast String Kernels using Inexact Matching for Protein Sequence, JMLR 2004 | ||
| BioBasics | NIPS09: Locality-Sensitive Binary Codes from Shift-Invariant Kernels | ||
| MedSignal | Segmenting Time Series: A Survey and Novel Approach, |
[43]: domain-adaptation
Table of readings
read on: - 12 Oct 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Xueying | Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data, ICLR17 1 | ||
| Bargav | Deep Learning with Differential Privacy, CCS16 2 | PDF + video | |
| Bargav | Privacy-Preserving Deep Learning, CCS15 3 | ||
| Xueying | Domain Separation Networks, NIPS16 4 |
[44]: dynamic
Table of readings
read on: - 25 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Edge | MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications | ||
| Edge | XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks | URL | Ryan PDF |
| Edge | DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices | Eamon PDF | |
| Edge | Loss-aware Binarization of Deep Networks, ICLR17 | Ryan PDF | |
| Edge | Espresso: Efficient Forward Propagation for Binary Deep Neural Networks | Eamon PDF | |
| Dynamic | Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution | Weilin PDF | |
| Dynamic | Dynamic Scheduling For Dynamic Control Flow in Deep Learning Systems | ||
| Dynamic | Cavs: An Efficient Runtime System for Dynamic Neural Networks |
read on: - 22 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Scalable | FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling | Ryan PDF + Arshdeep Pdf | |
| Scalable | MILE: A Multi-Level Framework for Scalable Graph Embedding | Ryan PDF | |
| Scalable | LanczosNet: Multi-Scale Deep Graph Convolutional Networks | Ryan PDF | |
| Scalable | Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis | Derrick PDF | |
| Scalable | Towards Federated learning at Scale: System Design | URL | Derrick PDF |
| Scalable | DNN Dataflow Choice Is Overrated | Derrick PDF | |
| Scalable | Towards Efficient Large-Scale Graph Neural Network Computing | Derrick PDF | |
| Scalable | PyTorch Geometric | URL | |
| Scalable | PyTorch BigGraph | URL | |
| Scalable | Simplifying Graph Convolutional Networks | ||
| Scalable | Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks |
read on: - 22 Feb 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| spherical | Spherical CNNs | Fuwen PDF + Arshdeep Pdf | |
| dynamic | Dynamic graph cnn for learning on point clouds, 2018 | Fuwen PDF | |
| basics | Geometric Deep Learning (simple introduction video) | URL | |
| matching | All Graphs Lead to Rome: Learning Geometric and Cycle-Consistent Representations with Graph Convolutional Networks | Fuwen PDF | |
| completion | Geometric matrix completion with recurrent multi-graph neural networks | Fuwen PDF | |
| Tutorial | Geometric Deep Learning on Graphs and Manifolds | URL | Arsh PDF |
| matching | Similarity Learning with Higher-Order Proximity for Brain Network Analysis | Arsh PDF | |
| pairwise | Pixel to Graph with Associative Embedding | Fuwen PDF | |
| 3D | 3D steerable cnns: Learning rotationally equivariant features in volumetric data | URL | Fuwen PDF |
read on: - 25 Oct 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | Learning Transferable Architectures for Scalable Image Recognition | ||
| Arshdeep | FractalNet: Ultra-Deep Neural Networks without Residuals |
read on: - 07 Nov 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| GaoJi | Forward and Reverse Gradient-Based Hyperparameter Optimization, ICML17 1 | ||
| Chaojiang | Adaptive Neural Networks for Efficient Inference, ICML17 2 | ||
| Bargav | Practical Gauss-Newton Optimisation for Deep Learning, ICML17 3 | ||
| Rita | How to Escape Saddle Points Efficiently, ICML17 4 | ||
| Batched High-dimensional Bayesian Optimization via Structural Kernel Learning |
read on: - 05 Oct 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Anant | AdaNet: Adaptive Structural Learning of Artificial Neural Networks, ICML17 1 | ||
| Shijia | SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization, ICML17 2 | ||
| Jack | Proximal Deep Structured Models, NIPS16 3 | ||
| Optimal Architectures in a Solvable Model of Deep Networks, NIPS16 4 | |||
| Tianlu | Large-Scale Evolution of Image Classifiers, ICML17 5 |
read on: - 03 Oct 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Tianlu | Ask Me Anything: Dynamic Memory Networks for Natural Language Processing, ICML17 1 | PDF + code | |
| Jack | Reasoning with Memory Augmented Neural Networks for Language Comprehension, ICLR17 2 | ||
| Xueying | State-Frequency Memory Recurrent Neural Networks, ICML17 3 |
read on: - 26 Sep 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Rita | Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer, ICLR17 1 | ||
| Tianlu | Dynamic Coattention Networks For Question Answering, ICLR17 2 | PDF + code | |
| ChaoJiang | Structured Attention Networks, ICLR17 3 | PDF + code |
read on: - 22 Jun 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction 1 | ||
| Arshdeep | Decoupled Neural Interfaces Using Synthetic Gradients 2 | ||
| Arshdeep | Diet Networks: Thin Parameters for Fat Genomics 3 | ||
| Arshdeep | Metric Learning with Adaptive Density Discrimination 4 |
read on: - 02 Jun 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | HyperNetworks, David Ha, Andrew Dai, Quoc V. Le ICLR 2017 1 | ||
| Arshdeep | Learning feed-forward one-shot learners 2 | ||
| Arshdeep | Learning to Learn by gradient descent by gradient descent 3 | ||
| Arshdeep | Dynamic Filter Networks 4 https://arxiv.org/abs/1605.09673 |
[45]: ehr
Table of readings
read on: - 20 Dec 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Bill | Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning | ||
| Chao | Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis (I) | ||
| Chao | Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis (II) | ||
| Derrick | Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis (III) | ||
| Chao | Reading Wikipedia to Answer Open-Domain Questions | ||
| Jennifer | Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text |
read on: - 02 Dec 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Jennifer | Adversarial Attacks Against Medical Deep Learning Systems | ||
| Jennifer | Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learning | ||
| Jennifer | Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers | ||
| Jennifer | CleverHans | ||
| Ji | Ji-f18-New papers about adversarial attack |
[46]: em
Table of readings
read on: - 22 Apr 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Muthu | Optimization Methods for Large-Scale Machine Learning, Léon Bottou, Frank E. Curtis, Jorge Nocedal 1 | ||
| Muthu | Fast Training of Recurrent Networks Based on EM Algorithm (1998) 2 | ||
| Muthu | FitNets: Hints for Thin Deep Nets, ICLR15 3 | ||
| Muthu | Two NIPS 2015 Deep Learning Optimization Papers | ||
| Muthu | Difference Target Propagation (2015) 4 |
[47]: embedding
Table of readings
read on: - 22 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Scalable | FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling | Ryan PDF + Arshdeep Pdf | |
| Scalable | MILE: A Multi-Level Framework for Scalable Graph Embedding | Ryan PDF | |
| Scalable | LanczosNet: Multi-Scale Deep Graph Convolutional Networks | Ryan PDF | |
| Scalable | Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis | Derrick PDF | |
| Scalable | Towards Federated learning at Scale: System Design | URL | Derrick PDF |
| Scalable | DNN Dataflow Choice Is Overrated | Derrick PDF | |
| Scalable | Towards Efficient Large-Scale Graph Neural Network Computing | Derrick PDF | |
| Scalable | PyTorch Geometric | URL | |
| Scalable | PyTorch BigGraph | URL | |
| Scalable | Simplifying Graph Convolutional Networks | ||
| Scalable | Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks |
read on: - 17 Feb 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Program | Neural network-based graph embedding for cross-platform binary code similarity detection | Pdf + Pdf | Faizan PDF + GaoJi Pdf |
| Program | Deep Program Reidentification: A Graph Neural Network Solution | Weilin PDF | |
| Program | Heterogeneous Graph Neural Networks for Malicious Account Detection | Weilin Pdf | |
| Program | Learning to represent programs with graphs | Pdf 1 |
read on: - 25 Jan 2019
| Presenter | Papers | Paper URL | Our Notes |
|---|---|---|---|
| Basics | GraphSAGE: Large-scale Graph Representation Learning by Jure Leskovec Stanford University | URL + PDF | |
| Basics | Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering by Xavier Bresson | URL + PDF | Ryan Pdf |
| Basics | Gated Graph Sequence Neural Networks by Microsoft Research | URL + PDF | Faizan Pdf |
| Basics | DeepWalk - Turning Graphs into Features via Network Embeddings | URL + PDF | |
| Basics | Spectral Networks and Locally Connected Networks on Graphs 1 | GaoJi slides + Bill Pdf | |
| Basics | A Comprehensive Survey on Graph Neural Networks/ Graph Neural Networks: A Review of Methods and Applications | Jack Pdf | |
| GCN | Semi-Supervised Classification with Graph Convolutional Networks | Jack Pdf |
read on: - 27 Aug 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Derrick | GloVe: Global Vectors for Word Representation | ||
| Derrick | PARL.AI: A unified platform for sharing, training and evaluating dialog models across many tasks. | URL | |
| Derrick | scalable nearest neighbor algorithms for high dimensional data (PAMI14) 1 | ||
| Derrick | StarSpace: Embed All The Things! | ||
| Derrick | Weaver: Deep Co-Encoding of Questions and Documents for Machine Reading, Martin Raison, Pierre-Emmanuel Mazaré, Rajarshi Das, Antoine Bordes |
read on: - 10 Jan 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Bill | Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation 1 | ||
| Bill | Measuring the tendency of CNNs to Learn Surface Statistical Regularities Jason Jo, Yoshua Bengio | ||
| Bill | Generating Sentences by Editing Prototypes, Kelvin Guu, Tatsunori B. Hashimoto, Yonatan Oren, Percy Liang 2 | ||
| Bill | On the importance of single directions for generalization, Ari S. Morcos, David G.T. Barrett, Neil C. Rabinowitz, Matthew Botvinick |
read on: - 12 Jan 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| QA | Learning to rank with (a lot of) word features | ||
| Relation | A semantic matching energy function for learning with multi-relational data | ||
| Relation | Translating embeddings for modeling multi-relational data | ||
| QA | Reading wikipedia to answer open-domain questions | ||
| QA | Question answering with subgraph embeddings |
read on: - 12 Jan 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| NLP | A Neural Probabilistic Language Model | ||
| Text | Bag of Tricks for Efficient Text Classification | ||
| Text | Character-level Convolutional Networks for Text Classification | ||
| NLP | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | ||
| seq2seq | Neural Machine Translation by Jointly Learning to Align and Translate | ||
| NLP | Natural Language Processing (almost) from Scratch | ||
| Train | Curriculum learning | ||
| Muthu | NeuroIPS Embedding Papers survey 2012 to 2015 | NIPS | |
| Basics | Efficient BackProp |
[48]: encoder-decoder
Table of readings
read on: - 09 Dec 2019
| Team INDEX | Title & Link | Tags | Our Slide |
|---|---|---|---|
| T14 | CAN: Creative Adversarial Networks Generating “Art” | GAN | OurSlide |
| T26 | Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation | encoder-decoder, dialog, VAE, Interpretable | OurSlide |
| T32 | Which Training Methods for GANs do actually Converge | convergence, optimization, GAN | OurSlide |
[49]: expressive
Table of readings
read on: - 14 Sep 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| SE | Equivariance Through Parameter-Sharing, ICML17 1 | ||
| SE | Why Deep Neural Networks for Function Approximation?, ICLR17 2 | ||
| SE | Geometry of Neural Network Loss Surfaces via Random Matrix Theory, 3ICML17 | ||
| Sharp Minima Can Generalize For Deep Nets, ICML17 4 |
read on: - 12 Sep 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Ceyer | A Closer Look at Memorization in Deep Networks, ICML17 1 | ||
| On the Expressive Efficiency of Overlapping Architectures of Deep Learning 2 | DLSSpdf + video | ||
| Mutual Information | Opening the Black Box of Deep Neural Networks via Information 3 | URL + video | |
| ChaoJiang | Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity, NIPS16 |
read on: - 05 Sep 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Rita | On the Expressive Power of Deep Neural Networks 1 | ||
| Arshdeep | Understanding deep learning requires rethinking generalization, ICLR17 2 | ||
| Tianlu | On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima, ICLR17 3 |
[50]: few-shot
Table of readings
read on: - 02 Nov 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| GaoJi | Neural Architecture Search with Reinforcement Learning, ICLR17 1 | ||
| Ceyer | Learning to learn 2 | DLSS17video | |
| Beilun | Optimization as a Model for Few-Shot Learning, ICLR17 3 | PDF + More | |
| Anant | Neural Optimizer Search with Reinforcement Learning, ICML17 4 |
read on: - 18 Jan 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| seq2seq | Sequence to Sequence Learning with Neural Networks | ||
| Set | Pointer Networks | ||
| Set | Order Matters: Sequence to Sequence for Sets | ||
| Point Attention | Multiple Object Recognition with Visual Attention | ||
| Memory | End-To-End Memory Networks | Jack Survey | |
| Memory | Neural Turing Machines | ||
| Memory | Hybrid computing using a neural network with dynamic external memory | ||
| Muthu | Matching Networks for One Shot Learning (NIPS16) 1 | ||
| Jack | Meta-Learning with Memory-Augmented Neural Networks (ICML16) 2 | ||
| Metric | ICML07 Best Paper - Information-Theoretic Metric Learning |
[51]: forcing
Table of readings
[52]: forgetting
Table of readings
read on: - 12 Dec 2019
| Team INDEX | Title & Link | Tags | Our Slide |
|---|---|---|---|
| T2 | Empirical Study of Example Forgetting During Deep Neural Network Learning | Sample Selection, forgetting | OurSlide |
| T29 | Select Via Proxy: Efficient Data Selection For Training Deep Networks | Sample Selection | OurSlide |
| T9 | How SGD Selects the Global Minima in over-parameterized Learning | optimization | OurSlide |
| T10 | Escaping Saddles with Stochastic Gradients | optimization | OurSlide |
| T13 | To What Extent Do Different Neural Networks Learn the Same Representation | subspace | OurSlide |
| T19 | On the Information Bottleneck Theory of Deep Learning | informax | OurSlide |
| T20 | Visualizing the Loss Landscape of Neural Nets | normalization | OurSlide |
| T21 | Using Pre-Training Can Improve Model Robustness and Uncertainty | training, analysis | OurSlide |
| T24 | Norm matters: efficient and accurate normalization schemes in deep networks | normalization | OurSlide |
[53]: fuzzing
Table of readings
read on: - 03 Aug 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| GaoJi | Deep Reinforcement Fuzzing, Konstantin Böttinger, Patrice Godefroid, Rishabh Singh | ||
| GaoJi | Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks, Guy Katz, Clark Barrett, David Dill, Kyle Julian, Mykel Kochenderfer | ||
| GaoJi | DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars, Yuchi Tian, Kexin Pei, Suman Jana, Baishakhi Ray | ||
| GaoJi | A few Recent (2018) papers on Black-box Adversarial Attacks, like Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors 1 | ||
| GaoJi | A few Recent papers of Adversarial Attacks on reinforcement learning, like Adversarial Attacks on Neural Network Policies (Sandy Huang, Nicolas Papernot, Ian Goodfellow, Yan Duan, Pieter Abbeel) | ||
| Testing | DeepXplore: Automated Whitebox Testing of Deep Learning Systems |
[54]: gan
Table of readings
read on: - 09 Dec 2019
| Team INDEX | Title & Link | Tags | Our Slide |
|---|---|---|---|
| T14 | CAN: Creative Adversarial Networks Generating “Art” | GAN | OurSlide |
| T26 | Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation | encoder-decoder, dialog, VAE, Interpretable | OurSlide |
| T32 | Which Training Methods for GANs do actually Converge | convergence, optimization, GAN | OurSlide |
read on: - 29 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| QA | A Comparison of Current Graph Database Models | Pdf + PDF2 | Bill PDF |
| QA | Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text | Bill [PDF + GaoJi Pdf | |
| QA | Generative Question Answering: Learning to Answer the Whole Question, Mike Lewis, Angela Fan | Bill PDF + GaoJi Pdf | |
| QA | Learning to Reason Science Exam Questions with Contextual Knowledge Graph Embeddings / Knowledge Graph Embedding via Dynamic Mapping Matrix | PDF + Pdf | Bill PDF + GaoJi Pdf |
| Text | Adversarial Text Generation via Feature-Mover’s Distance | URL | Faizan PDF |
| Text | Content preserving text generation with attribute controls | URL | Faizan PDF |
| Text | Multiple-Attribute Text Rewriting, ICLR, 2019, | URL | Faizan PDF |
| Text | Writeprints: a stylometric approach to identity level identification and similarity detection in cyberSpace | URL | Faizan PDF |
read on: - 15 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Generate | Maximum-Likelihood Augmented Discrete Generative Adversarial Networks | Tkach PDF + GaoJi Pdf | |
| Generate | Graphical Generative Adversarial Networks | Arshdeep PDF | |
| Generate | GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models, ICML2018 | Arshdeep PDF | |
| Generate | Inference in probabilistic graphical models by Graph Neural Networks | Arshdeep PDF | |
| Generate | Encoding robust representation for graph generation | Arshdeep PDF | |
| Generate | Junction Tree Variational Autoencoder for Molecular Graph Generation | Tkach PDF + Arshdeep Pdf | |
| Generate | Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation NeurIPS18 | Tkach PDF | |
| Generate | Towards Variational Generation of Small Graphs | Tkach PDF + Arshdeep Pdf | |
| Generate | Convolutional Imputation of Matrix Networks | Tkach PDF | |
| Generate | Graph Convolutional Matrix Completion | Tkach PDF | |
| Generate | NetGAN: Generating Graphs via Random Walks ICML18 | [ULR] | Tkach PDF |
| Beam | Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement | URL | Tkach PDF |
read on: - 29 Dec 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Tkach | Boundary-Seeking Generative Adversarial Networks | ||
| Tkach | Maximum-Likelihood Augmented Discrete Generative Adversarial Networks | ||
| Tkach | Generating Sentences from a Continuous Space |
read on: - 21 Dec 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | Constrained Graph Variational Autoencoders for Molecule Design | ||
| Arshdeep | Learning Deep Generative Models of Graphs | ||
| Arshdeep | Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation | ||
| Jack | Generating and designing DNA with deep generative models |
read on: - 12 Oct 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Jack | A Unified Approach to Interpreting Model Predictions | ||
| Jack | “Why Should I Trust You?”: Explaining the Predictions of Any Classifier | ||
| Jack | Visual Feature Attribution using Wasserstein GANs | ||
| Jack | GAN Dissection: Visualizing and Understanding Generative Adversarial Networks | ||
| GaoJi | Recent Interpretable machine learning papers | ||
| Jennifer | The Building Blocks of Interpretability |
read on: - 23 Aug 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables, Chris J. Maddison, Andriy Mnih, Yee Whye Teh 1 | ||
| GaoJi | Summary Of Several Autoencoder models | ||
| GaoJi | Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models, Jesse Engel, Matthew Hoffman, Adam Roberts 2 | ||
| GaoJi | Summary of A Few Recent Papers about Discrete Generative models, SeqGAN, MaskGAN, BEGAN, BoundaryGAN | ||
| Arshdeep | Semi-Amortized Variational Autoencoders, Yoon Kim, Sam Wiseman, Andrew C. Miller, David Sontag, Alexander M. Rush 3 | ||
| Arshdeep | Synthesizing Programs for Images using Reinforced Adversarial Learning, Yaroslav Ganin, Tejas Kulkarni, Igor Babuschkin, S.M. Ali Eslami, Oriol Vinyals 4 |
read on: - 20 Apr 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| BrandonLiu | Summary of Recent Generative Adversarial Networks (Classified) | ||
| Jack | Generating and designing DNA with deep generative models, Nathan Killoran, Leo J. Lee, Andrew Delong, David Duvenaud, Brendan J. Frey | ||
| GaoJi | More about basics of GAN | ||
| McGan: Mean and Covariance Feature Matching GAN, PMLR 70:2527-2535 | |||
| Wasserstein GAN, ICML17 | |||
| Geometrical Insights for Implicit Generative Modeling, L Bottou, M Arjovsky, D Lopez-Paz, M Oquab |
[55]: gcn
Table of readings
read on: - 05 Jan 2020
| Index | Papers | Our Slides |
|---|---|---|
| 1 | Graph Convolutions: More than You Wanted to Know | Derrick Survey |
| 2 | Spectral Graph Sparsification | Derrick Survey |
| 3 | Complexity Analysis of Graph Convolutional Networks and in Attention based GNN | Derrick Survey |
| 4 | PyTorch-BigGraph: A Large-Scale Graph Embedding System | Derrick Survey |
| 5 | Scalable GNN Updates: More About PyTorch Geometric (PyG) | Derrick Survey |
| 6 | Time and Space Complexity of Graph Convolutional Networks | Derrick Survey |
| 7 | Large Scale GNN and Transformer Models and for Genomics | Jack Survey |
| 8 | Long Range Attention and Visualizing BERT | Jak Survey |
| 9 | Benchmarking Graph Neural Networks | Sanchit Survey |
[56]: gene-network
Table of readings
read on: - 05 Jun 2020
| Index | Papers | Our Slides |
|---|---|---|
| 1 | Protein 3D Structure Computed from Evolutionary Sequence Variation | Arsh Survey |
| 3 | Regulatory network inference on developmental and evolutionary lineages | Arsh Survey |
| 4 | Deep learning in ultrasound image analysis | Zhe Survey |
| 5 | Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning (DeepBind) | Jack Survey |
| 6 | Canonical and single-cell Hi-C reveal distinct chromatin interaction sub-networks of mammalian transcription factors | Jack Survey |
| 7 | BindSpace decodes transcription factor binding signals by large-scale sequence embedding | Jack Survey |
| 8 | FastXML: A Fast, Accurate and Stable Tree-classifier for eXtreme Multi-label Learning | Jack Survey |
| 9 | Query-Reduction Networks for Question Answering | Bill Survey |
[57]: generalization
Table of readings
read on: - 05 Apr 2020
| Index | Papers | Our Slides |
|---|---|---|
| 1 | Invariant Risk Minimization | Zhe Survey |
| 2 | Causal Machine Learning | Zhe Survey |
| 3 | A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms | Zhe Survey |
| 3 | Review on Optimization-Based Meta Learning | Zhe Survey |
| 4 | Domain adaptation and counterfactual prediction | Zhe Survey |
| 5 | Gaussian Processes | Zhe Survey |
| 6 | A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data | Zhe Survey |
| 7 | Few-shot domain adaptation by causal mechanism transfer | Zhe Survey |
read on: - 05 Mar 2020
| Index | Papers | Our Slides |
|---|---|---|
| 1 | Actor-Critic Methods for Control | Jake Survey |
| 2 | Generalization in Deep Reinforcement Learning | Jake Survey |
| 3 | Sample Efficient RL (Part 1) | Jake Survey |
| 4 | Sample Efficient RL (Part 2) | Jake Survey |
| 5 | Model-Free Value Methods in Deep RL | Jake Survey |
| 6 | Investigating Human Priors for Playing Video Games | Arsh Survey |
Table of readings
read on: - 12 Dec 2019
| Team INDEX | Title & Link | Tags | Our Slide |
|---|---|---|---|
| T2 | Empirical Study of Example Forgetting During Deep Neural Network Learning | Sample Selection, forgetting | OurSlide |
| T29 | Select Via Proxy: Efficient Data Selection For Training Deep Networks | Sample Selection | OurSlide |
| T9 | How SGD Selects the Global Minima in over-parameterized Learning | optimization | OurSlide |
| T10 | Escaping Saddles with Stochastic Gradients | optimization | OurSlide |
| T13 | To What Extent Do Different Neural Networks Learn the Same Representation | subspace | OurSlide |
| T19 | On the Information Bottleneck Theory of Deep Learning | informax | OurSlide |
| T20 | Visualizing the Loss Landscape of Neural Nets | normalization | OurSlide |
| T21 | Using Pre-Training Can Improve Model Robustness and Uncertainty | training, analysis | OurSlide |
| T24 | Norm matters: efficient and accurate normalization schemes in deep networks | normalization | OurSlide |
read on: - 23 Aug 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables, Chris J. Maddison, Andriy Mnih, Yee Whye Teh 1 | ||
| GaoJi | Summary Of Several Autoencoder models | ||
| GaoJi | Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models, Jesse Engel, Matthew Hoffman, Adam Roberts 2 | ||
| GaoJi | Summary of A Few Recent Papers about Discrete Generative models, SeqGAN, MaskGAN, BEGAN, BoundaryGAN | ||
| Arshdeep | Semi-Amortized Variational Autoencoders, Yoon Kim, Sam Wiseman, Andrew C. Miller, David Sontag, Alexander M. Rush 3 | ||
| Arshdeep | Synthesizing Programs for Images using Reinforced Adversarial Learning, Yaroslav Ganin, Tejas Kulkarni, Igor Babuschkin, S.M. Ali Eslami, Oriol Vinyals 4 |
read on: - 20 Apr 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| BrandonLiu | Summary of Recent Generative Adversarial Networks (Classified) | ||
| Jack | Generating and designing DNA with deep generative models, Nathan Killoran, Leo J. Lee, Andrew Delong, David Duvenaud, Brendan J. Frey | ||
| GaoJi | More about basics of GAN | ||
| McGan: Mean and Covariance Feature Matching GAN, PMLR 70:2527-2535 | |||
| Wasserstein GAN, ICML17 | |||
| Geometrical Insights for Implicit Generative Modeling, L Bottou, M Arjovsky, D Lopez-Paz, M Oquab |
read on: - 10 Jan 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Bill | Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation 1 | ||
| Bill | Measuring the tendency of CNNs to Learn Surface Statistical Regularities Jason Jo, Yoshua Bengio | ||
| Bill | Generating Sentences by Editing Prototypes, Kelvin Guu, Tatsunori B. Hashimoto, Yonatan Oren, Percy Liang 2 | ||
| Bill | On the importance of single directions for generalization, Ari S. Morcos, David G.T. Barrett, Neil C. Rabinowitz, Matthew Botvinick |
read on: - 16 Nov 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | Generalization and Equilibrium in Generative Adversarial Nets (ICML17) 1 | PDF + video | |
| Arshdeep | Mode Regularized Generative Adversarial Networks (ICLR17) 2 | ||
| Bargav | Improving Generative Adversarial Networks with Denoising Feature Matching, ICLR17 3 | ||
| Anant | Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy, ICLR17 4 | PDF + code |
read on: - 14 Sep 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| SE | Equivariance Through Parameter-Sharing, ICML17 1 | ||
| SE | Why Deep Neural Networks for Function Approximation?, ICLR17 2 | ||
| SE | Geometry of Neural Network Loss Surfaces via Random Matrix Theory, 3ICML17 | ||
| Sharp Minima Can Generalize For Deep Nets, ICML17 4 |
read on: - 05 Sep 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Rita | On the Expressive Power of Deep Neural Networks 1 | ||
| Arshdeep | Understanding deep learning requires rethinking generalization, ICLR17 2 | ||
| Tianlu | On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima, ICLR17 3 |
[58]: generative
Table of readings
read on: - 05 Feb 2020
| Index | Papers | Our Slides |
|---|---|---|
| 1 | Beta VAE, Ladder VAE, Causal VAE | Arsh Survey |
| 2 | Learnt Prior VAE | Arsh Survey |
| 3 | Multitask Graph Autoencoder | Arsh Survey |
| 4 | Introduction to component analysi | Zhe Survey |
| 5 | Normalizing flow | Zhe Survey |
| 6 | Nonlinear ICA | Zhe Survey |
| 7 | Deep Convolutional Inverse Graphics Network | Zhe Survey |
Table of readings
read on: - 29 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| QA | A Comparison of Current Graph Database Models | Pdf + PDF2 | Bill PDF |
| QA | Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text | Bill [PDF + GaoJi Pdf | |
| QA | Generative Question Answering: Learning to Answer the Whole Question, Mike Lewis, Angela Fan | Bill PDF + GaoJi Pdf | |
| QA | Learning to Reason Science Exam Questions with Contextual Knowledge Graph Embeddings / Knowledge Graph Embedding via Dynamic Mapping Matrix | PDF + Pdf | Bill PDF + GaoJi Pdf |
| Text | Adversarial Text Generation via Feature-Mover’s Distance | URL | Faizan PDF |
| Text | Content preserving text generation with attribute controls | URL | Faizan PDF |
| Text | Multiple-Attribute Text Rewriting, ICLR, 2019, | URL | Faizan PDF |
| Text | Writeprints: a stylometric approach to identity level identification and similarity detection in cyberSpace | URL | Faizan PDF |
read on: - 15 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Generate | Maximum-Likelihood Augmented Discrete Generative Adversarial Networks | Tkach PDF + GaoJi Pdf | |
| Generate | Graphical Generative Adversarial Networks | Arshdeep PDF | |
| Generate | GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models, ICML2018 | Arshdeep PDF | |
| Generate | Inference in probabilistic graphical models by Graph Neural Networks | Arshdeep PDF | |
| Generate | Encoding robust representation for graph generation | Arshdeep PDF | |
| Generate | Junction Tree Variational Autoencoder for Molecular Graph Generation | Tkach PDF + Arshdeep Pdf | |
| Generate | Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation NeurIPS18 | Tkach PDF | |
| Generate | Towards Variational Generation of Small Graphs | Tkach PDF + Arshdeep Pdf | |
| Generate | Convolutional Imputation of Matrix Networks | Tkach PDF | |
| Generate | Graph Convolutional Matrix Completion | Tkach PDF | |
| Generate | NetGAN: Generating Graphs via Random Walks ICML18 | [ULR] | Tkach PDF |
| Beam | Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement | URL | Tkach PDF |
read on: - 29 Dec 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Tkach | Boundary-Seeking Generative Adversarial Networks | ||
| Tkach | Maximum-Likelihood Augmented Discrete Generative Adversarial Networks | ||
| Tkach | Generating Sentences from a Continuous Space |
read on: - 21 Dec 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | Constrained Graph Variational Autoencoders for Molecule Design | ||
| Arshdeep | Learning Deep Generative Models of Graphs | ||
| Arshdeep | Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation | ||
| Jack | Generating and designing DNA with deep generative models |
read on: - 13 Oct 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | deepCRISPR: optimized CRISPR guide RNA design by deep learning , Genome Biology 2018 | ||
| Arshdeep | The CRISPR tool kit for genome editing and beyond, Mazhar Adli | ||
| Eric | Intro of Genetic Engineering | ||
| Eric | Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs | ||
| Brandon | Generative Modeling for Protein Structure | URL |
read on: - 23 Aug 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables, Chris J. Maddison, Andriy Mnih, Yee Whye Teh 1 | ||
| GaoJi | Summary Of Several Autoencoder models | ||
| GaoJi | Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models, Jesse Engel, Matthew Hoffman, Adam Roberts 2 | ||
| GaoJi | Summary of A Few Recent Papers about Discrete Generative models, SeqGAN, MaskGAN, BEGAN, BoundaryGAN | ||
| Arshdeep | Semi-Amortized Variational Autoencoders, Yoon Kim, Sam Wiseman, Andrew C. Miller, David Sontag, Alexander M. Rush 3 | ||
| Arshdeep | Synthesizing Programs for Images using Reinforced Adversarial Learning, Yaroslav Ganin, Tejas Kulkarni, Igor Babuschkin, S.M. Ali Eslami, Oriol Vinyals 4 |
read on: - 13 Aug 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. | ||
| Arshdeep | Solving the RNA design problem with reinforcement learning, PLOSCB 1 | ||
| Arshdeep | Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk 2 | ||
| Arshdeep | Towards Gene Expression Convolutions using Gene Interaction Graphs, Francis Dutil, Joseph Paul Cohen, Martin Weiss, Georgy Derevyanko, Yoshua Bengio 3 | ||
| Brandon | Kipoi: Accelerating the Community Exchange and Reuse of Predictive Models for Genomics | ||
| Arshdeep | Feedback GAN (FBGAN) for DNA: a Novel Feedback-Loop Architecture for Optimizing Protein Functions 2 |
read on: - 12 May 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Bill | Intriguing Properties of Adversarial Examples, Ekin D. Cubuk, Barret Zoph, Samuel S. Schoenholz, Quoc V. Le 1 | ||
| Bill | Adversarial Spheres 2 | ||
| Bill | Adversarial Transformation Networks: Learning to Generate Adversarial Examples, Shumeet Baluja, Ian Fischer 3 | ||
| Bill | Thermometer encoding: one hot way to resist adversarial examples 4 | ||
| Adversarial Logit Pairing , Harini Kannan, Alexey Kurakin, Ian Goodfellow 5 |
read on: - 20 Apr 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| BrandonLiu | Summary of Recent Generative Adversarial Networks (Classified) | ||
| Jack | Generating and designing DNA with deep generative models, Nathan Killoran, Leo J. Lee, Andrew Delong, David Duvenaud, Brendan J. Frey | ||
| GaoJi | More about basics of GAN | ||
| McGan: Mean and Covariance Feature Matching GAN, PMLR 70:2527-2535 | |||
| Wasserstein GAN, ICML17 | |||
| Geometrical Insights for Implicit Generative Modeling, L Bottou, M Arjovsky, D Lopez-Paz, M Oquab |
read on: - 10 Jan 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Bill | Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation 1 | ||
| Bill | Measuring the tendency of CNNs to Learn Surface Statistical Regularities Jason Jo, Yoshua Bengio | ||
| Bill | Generating Sentences by Editing Prototypes, Kelvin Guu, Tatsunori B. Hashimoto, Yonatan Oren, Percy Liang 2 | ||
| Bill | On the importance of single directions for generalization, Ari S. Morcos, David G.T. Barrett, Neil C. Rabinowitz, Matthew Botvinick |
read on: - 16 Nov 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | Generalization and Equilibrium in Generative Adversarial Nets (ICML17) 1 | PDF + video | |
| Arshdeep | Mode Regularized Generative Adversarial Networks (ICLR17) 2 | ||
| Bargav | Improving Generative Adversarial Networks with Denoising Feature Matching, ICLR17 3 | ||
| Anant | Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy, ICLR17 4 | PDF + code |
read on: - 14 Nov 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| ChaoJiang | Courville - Generative Models II | DLSS17Slide + video | |
| GaoJi | Attend, Infer, Repeat: Fast Scene Understanding with Generative Models, NIPS16 1 | PDF + talk | |
| Arshdeep | Composing graphical models with neural networks for structured representations and fast inference, NIPS16 2 | ||
| Johnson - Graphical Models and Deep Learning | DLSSSlide + video | ||
| Parallel Multiscale Autoregressive Density Estimation, ICML17 3 | |||
| Beilun | Conditional Image Generation with Pixel CNN Decoders, NIPS16 4 | ||
| Shijia | Marrying Graphical Models & Deep Learning | DLSS17 + Video |
read on: - 28 Sep 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Jack | Attend, Adapt and Transfer: Attentive Deep Architecture for Adaptive Transfer from multiple sources in the same domain, ICLR17 1 | ||
| Arshdeep | Bidirectional Attention Flow for Machine Comprehension, ICLR17 2 | PDF + code | |
| Ceyer | Image-to-Markup Generation with Coarse-to-Fine Attention, ICML17 | PDF + code | |
| ChaoJiang | Can Active Memory Replace Attention? ; Samy Bengio, NIPS16 3 | ||
| An Information-Theoretic Framework for Fast and Robust Unsupervised Learning via Neural Population Infomax, ICLR17 |
[59]: genomics
Table of readings
read on: - 13 Oct 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | deepCRISPR: optimized CRISPR guide RNA design by deep learning , Genome Biology 2018 | ||
| Arshdeep | The CRISPR tool kit for genome editing and beyond, Mazhar Adli | ||
| Eric | Intro of Genetic Engineering | ||
| Eric | Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs | ||
| Brandon | Generative Modeling for Protein Structure | URL |
read on: - 13 Aug 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. | ||
| Arshdeep | Solving the RNA design problem with reinforcement learning, PLOSCB 1 | ||
| Arshdeep | Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk 2 | ||
| Arshdeep | Towards Gene Expression Convolutions using Gene Interaction Graphs, Francis Dutil, Joseph Paul Cohen, Martin Weiss, Georgy Derevyanko, Yoshua Bengio 3 | ||
| Brandon | Kipoi: Accelerating the Community Exchange and Reuse of Predictive Models for Genomics | ||
| Arshdeep | Feedback GAN (FBGAN) for DNA: a Novel Feedback-Loop Architecture for Optimizing Protein Functions 2 |
[60]: geometric
Table of readings
read on: - 22 Feb 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| spherical | Spherical CNNs | Fuwen PDF + Arshdeep Pdf | |
| dynamic | Dynamic graph cnn for learning on point clouds, 2018 | Fuwen PDF | |
| basics | Geometric Deep Learning (simple introduction video) | URL | |
| matching | All Graphs Lead to Rome: Learning Geometric and Cycle-Consistent Representations with Graph Convolutional Networks | Fuwen PDF | |
| completion | Geometric matrix completion with recurrent multi-graph neural networks | Fuwen PDF | |
| Tutorial | Geometric Deep Learning on Graphs and Manifolds | URL | Arsh PDF |
| matching | Similarity Learning with Higher-Order Proximity for Brain Network Analysis | Arsh PDF | |
| pairwise | Pixel to Graph with Associative Embedding | Fuwen PDF | |
| 3D | 3D steerable cnns: Learning rotationally equivariant features in volumetric data | URL | Fuwen PDF |
read on: - 15 Feb 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Bio | KDEEP: Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks, 2018 1 | Eli Pdf | |
| Bio | Molecular geometry prediction using a deep generative graph neural network | Eli Pdf | |
| Bio | Visualizing convolutional neural network protein-ligand scoring | PDF() | Eli PDF |
| Bio | Deep generative models of genetic variation capture mutation effects | PDF() | Eli PDF |
| Bio | Attentive cross-modal paratope prediction | Eli PDF |
[61]: graph
Table of readings
read on: - 05 Jan 2020
| Index | Papers | Our Slides |
|---|---|---|
| 1 | A Flexible Generative Framework for Graph-based Semi-supervised Learning | Arsh Survey |
| 2 | Learning Discrete Structures for Graph Neural Networks | Arsh Survey |
| 4 | Graph Markov Neural Nets | Arsh Survey |
| Graph Markov Neural Networks | Jack Survey | |
| 5 | GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations | Arsh Survey |
| 6 | Subgraph Neural Networks | Arsh Survey |
| 7 | Pointer Graph Networks | Arsh Survey |
| 8 | Modeling Relational Data with Graph Convolutional Networks | Arsh Survey |
| 9 | Graph Learning | Zhe Survey |
| 8 | Neural Relational Inference | Zhe Survey |
Table of readings
read on: - 29 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| QA | A Comparison of Current Graph Database Models | Pdf + PDF2 | Bill PDF |
| QA | Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text | Bill [PDF + GaoJi Pdf | |
| QA | Generative Question Answering: Learning to Answer the Whole Question, Mike Lewis, Angela Fan | Bill PDF + GaoJi Pdf | |
| QA | Learning to Reason Science Exam Questions with Contextual Knowledge Graph Embeddings / Knowledge Graph Embedding via Dynamic Mapping Matrix | PDF + Pdf | Bill PDF + GaoJi Pdf |
| Text | Adversarial Text Generation via Feature-Mover’s Distance | URL | Faizan PDF |
| Text | Content preserving text generation with attribute controls | URL | Faizan PDF |
| Text | Multiple-Attribute Text Rewriting, ICLR, 2019, | URL | Faizan PDF |
| Text | Writeprints: a stylometric approach to identity level identification and similarity detection in cyberSpace | URL | Faizan PDF |
read on: - 22 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Scalable | FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling | Ryan PDF + Arshdeep Pdf | |
| Scalable | MILE: A Multi-Level Framework for Scalable Graph Embedding | Ryan PDF | |
| Scalable | LanczosNet: Multi-Scale Deep Graph Convolutional Networks | Ryan PDF | |
| Scalable | Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis | Derrick PDF | |
| Scalable | Towards Federated learning at Scale: System Design | URL | Derrick PDF |
| Scalable | DNN Dataflow Choice Is Overrated | Derrick PDF | |
| Scalable | Towards Efficient Large-Scale Graph Neural Network Computing | Derrick PDF | |
| Scalable | PyTorch Geometric | URL | |
| Scalable | PyTorch BigGraph | URL | |
| Scalable | Simplifying Graph Convolutional Networks | ||
| Scalable | Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks |
read on: - 15 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Generate | Maximum-Likelihood Augmented Discrete Generative Adversarial Networks | Tkach PDF + GaoJi Pdf | |
| Generate | Graphical Generative Adversarial Networks | Arshdeep PDF | |
| Generate | GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models, ICML2018 | Arshdeep PDF | |
| Generate | Inference in probabilistic graphical models by Graph Neural Networks | Arshdeep PDF | |
| Generate | Encoding robust representation for graph generation | Arshdeep PDF | |
| Generate | Junction Tree Variational Autoencoder for Molecular Graph Generation | Tkach PDF + Arshdeep Pdf | |
| Generate | Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation NeurIPS18 | Tkach PDF | |
| Generate | Towards Variational Generation of Small Graphs | Tkach PDF + Arshdeep Pdf | |
| Generate | Convolutional Imputation of Matrix Networks | Tkach PDF | |
| Generate | Graph Convolutional Matrix Completion | Tkach PDF | |
| Generate | NetGAN: Generating Graphs via Random Walks ICML18 | [ULR] | Tkach PDF |
| Beam | Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement | URL | Tkach PDF |
read on: - 06 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Robust | Adversarial Attacks on Graph Structured Data | Faizan [PDF + GaoJi Pdf | |
| Robust | KDD’18 Adversarial Attacks on Neural Networks for Graph Data | Faizan PDF + GaoJi Pdf | |
| Robust | Attacking Binarized Neural Networks | Faizan PDF |
read on: - 22 Feb 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| spherical | Spherical CNNs | Fuwen PDF + Arshdeep Pdf | |
| dynamic | Dynamic graph cnn for learning on point clouds, 2018 | Fuwen PDF | |
| basics | Geometric Deep Learning (simple introduction video) | URL | |
| matching | All Graphs Lead to Rome: Learning Geometric and Cycle-Consistent Representations with Graph Convolutional Networks | Fuwen PDF | |
| completion | Geometric matrix completion with recurrent multi-graph neural networks | Fuwen PDF | |
| Tutorial | Geometric Deep Learning on Graphs and Manifolds | URL | Arsh PDF |
| matching | Similarity Learning with Higher-Order Proximity for Brain Network Analysis | Arsh PDF | |
| pairwise | Pixel to Graph with Associative Embedding | Fuwen PDF | |
| 3D | 3D steerable cnns: Learning rotationally equivariant features in volumetric data | URL | Fuwen PDF |
read on: - 01 Feb 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Matching | Deep Learning of Graph Matching, | PDF+ PDF | Jack Pdf |
| Matching | Graph Edit Distance Computation via Graph Neural Networks | Jack Pdf | |
| Basics | Link Prediction Based on Graph Neural Networks | Jack Pdf | |
| Basics | Supervised Community Detection with Line Graph Neural Networks | Jack Pdf | |
| Basics | Graph mining: Laws, generators, and algorithms | Arshdeep PDF | |
| pooling | Hierarchical graph representation learning with differentiable pooling | Eamon PDF |
read on: - 21 Dec 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | Constrained Graph Variational Autoencoders for Molecule Design | ||
| Arshdeep | Learning Deep Generative Models of Graphs | ||
| Arshdeep | Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation | ||
| Jack | Generating and designing DNA with deep generative models |
read on: - 20 Dec 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Bill | Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning | ||
| Chao | Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis (I) | ||
| Chao | Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis (II) | ||
| Derrick | Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis (III) | ||
| Chao | Reading Wikipedia to Answer Open-Domain Questions | ||
| Jennifer | Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text |
read on: - 16 Oct 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Eric | Modeling polypharmacy side effects with graph convolutional networks | ||
| Eric | Protein Interface Prediction using Graph Convolutional Networks | ||
| Eric | Structure biology meets data science: does anything change | URL | |
| Eric | DeepSite: protein-binding site predictor using 3D-convolutional neural networks | URL |
read on: - 12 Jan 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| QA | Learning to rank with (a lot of) word features | ||
| Relation | A semantic matching energy function for learning with multi-relational data | ||
| Relation | Translating embeddings for modeling multi-relational data | ||
| QA | Reading wikipedia to answer open-domain questions | ||
| QA | Question answering with subgraph embeddings |
[62]: graph-attention
Table of readings
read on: - 05 Jan 2020
| Index | Papers | Our Slides |
|---|---|---|
| 1 | Graph Convolutions: More than You Wanted to Know | Derrick Survey |
| 2 | Spectral Graph Sparsification | Derrick Survey |
| 3 | Complexity Analysis of Graph Convolutional Networks and in Attention based GNN | Derrick Survey |
| 4 | PyTorch-BigGraph: A Large-Scale Graph Embedding System | Derrick Survey |
| 5 | Scalable GNN Updates: More About PyTorch Geometric (PyG) | Derrick Survey |
| 6 | Time and Space Complexity of Graph Convolutional Networks | Derrick Survey |
| 7 | Large Scale GNN and Transformer Models and for Genomics | Jack Survey |
| 8 | Long Range Attention and Visualizing BERT | Jak Survey |
| 9 | Benchmarking Graph Neural Networks | Sanchit Survey |
[63]: graphical-model
Table of readings
read on: - 15 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Generate | Maximum-Likelihood Augmented Discrete Generative Adversarial Networks | Tkach PDF + GaoJi Pdf | |
| Generate | Graphical Generative Adversarial Networks | Arshdeep PDF | |
| Generate | GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models, ICML2018 | Arshdeep PDF | |
| Generate | Inference in probabilistic graphical models by Graph Neural Networks | Arshdeep PDF | |
| Generate | Encoding robust representation for graph generation | Arshdeep PDF | |
| Generate | Junction Tree Variational Autoencoder for Molecular Graph Generation | Tkach PDF + Arshdeep Pdf | |
| Generate | Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation NeurIPS18 | Tkach PDF | |
| Generate | Towards Variational Generation of Small Graphs | Tkach PDF + Arshdeep Pdf | |
| Generate | Convolutional Imputation of Matrix Networks | Tkach PDF | |
| Generate | Graph Convolutional Matrix Completion | Tkach PDF | |
| Generate | NetGAN: Generating Graphs via Random Walks ICML18 | [ULR] | Tkach PDF |
| Beam | Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement | URL | Tkach PDF |
read on: - 14 Nov 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| ChaoJiang | Courville - Generative Models II | DLSS17Slide + video | |
| GaoJi | Attend, Infer, Repeat: Fast Scene Understanding with Generative Models, NIPS16 1 | PDF + talk | |
| Arshdeep | Composing graphical models with neural networks for structured representations and fast inference, NIPS16 2 | ||
| Johnson - Graphical Models and Deep Learning | DLSSSlide + video | ||
| Parallel Multiscale Autoregressive Density Estimation, ICML17 3 | |||
| Beilun | Conditional Image Generation with Pixel CNN Decoders, NIPS16 4 | ||
| Shijia | Marrying Graphical Models & Deep Learning | DLSS17 + Video |
[64]: hash
Table of readings
read on: - 20 Jan 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| scalable | Sanjiv Kumar (Columbia EECS 6898), Lecture: Introduction to large-scale machine learning 2010 [^1] | ||
| data scalable | Alex Smola - Berkeley SML: Scalable Machine Learning: Syllabus 2012 [^2] | PDF 2014 + PDF | |
| Binary | Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 | ||
| Model | Binary embeddings with structured hashed projections 1 | ||
| Model | Deep Compression: Compressing Deep Neural Networks (ICLR 2016) 2 |
[65]: heterogeneous
Table of readings
read on: - 17 Feb 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Program | Neural network-based graph embedding for cross-platform binary code similarity detection | Pdf + Pdf | Faizan PDF + GaoJi Pdf |
| Program | Deep Program Reidentification: A Graph Neural Network Solution | Weilin PDF | |
| Program | Heterogeneous Graph Neural Networks for Malicious Account Detection | Weilin Pdf | |
| Program | Learning to represent programs with graphs | Pdf 1 |
[66]: hierarchical
Table of readings
read on: - 22 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Scalable | FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling | Ryan PDF + Arshdeep Pdf | |
| Scalable | MILE: A Multi-Level Framework for Scalable Graph Embedding | Ryan PDF | |
| Scalable | LanczosNet: Multi-Scale Deep Graph Convolutional Networks | Ryan PDF | |
| Scalable | Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis | Derrick PDF | |
| Scalable | Towards Federated learning at Scale: System Design | URL | Derrick PDF |
| Scalable | DNN Dataflow Choice Is Overrated | Derrick PDF | |
| Scalable | Towards Efficient Large-Scale Graph Neural Network Computing | Derrick PDF | |
| Scalable | PyTorch Geometric | URL | |
| Scalable | PyTorch BigGraph | URL | |
| Scalable | Simplifying Graph Convolutional Networks | ||
| Scalable | Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks |
read on: - 30 Nov 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Ceyer | Reinforcement Learning with Unsupervised Auxiliary Tasks, ICLR17 1 | ||
| Beilun | Why is Posterior Sampling Better than Optimism for Reinforcement Learning? Ian Osband, Benjamin Van Roy 2 | ||
| Ji | Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction, ICML17 3 | ||
| Xueying | End-to-End Differentiable Adversarial Imitation Learning, ICML17 4 | ||
| Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs, ICML17 | |||
| FeUdal Networks for Hierarchical Reinforcement Learning, ICML17 5 |
read on: - 17 Oct 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Jack | Learning to Query, Reason, and Answer Questions On Ambiguous Texts, ICLR17 1 | ||
| Arshdeep | Making Neural Programming Architectures Generalize via Recursion, ICLR17 2 | ||
| Xueying | Towards Deep Interpretability (MUS-ROVER II): Learning Hierarchical Representations of Tonal Music, ICLR17 3 |
[67]: high-dimensional
Table of readings
read on: - 23 Oct 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| GaoJi | Delving into Transferable Adversarial Examples and Black-box Attacks,ICLR17 1 | ||
| Shijia | On Detecting Adversarial Perturbations, ICLR17 2 | ||
| Anant | Parseval Networks: Improving Robustness to Adversarial Examples, ICML17 3 | ||
| Bargav | Being Robust (in High Dimensions) Can Be Practical, ICML17 4 |
[68]: hyperparameter
Table of readings
read on: - 25 Oct 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | Learning Transferable Architectures for Scalable Image Recognition | ||
| Arshdeep | FractalNet: Ultra-Deep Neural Networks without Residuals |
read on: - 07 Nov 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| GaoJi | Forward and Reverse Gradient-Based Hyperparameter Optimization, ICML17 1 | ||
| Chaojiang | Adaptive Neural Networks for Efficient Inference, ICML17 2 | ||
| Bargav | Practical Gauss-Newton Optimisation for Deep Learning, ICML17 3 | ||
| Rita | How to Escape Saddle Points Efficiently, ICML17 4 | ||
| Batched High-dimensional Bayesian Optimization via Structural Kernel Learning |
[69]: imitation-learning
Table of readings
read on: - 30 Nov 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Ceyer | Reinforcement Learning with Unsupervised Auxiliary Tasks, ICLR17 1 | ||
| Beilun | Why is Posterior Sampling Better than Optimism for Reinforcement Learning? Ian Osband, Benjamin Van Roy 2 | ||
| Ji | Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction, ICML17 3 | ||
| Xueying | End-to-End Differentiable Adversarial Imitation Learning, ICML17 4 | ||
| Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs, ICML17 | |||
| FeUdal Networks for Hierarchical Reinforcement Learning, ICML17 5 |
[70]: imputation
Table of readings
read on: - 15 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Generate | Maximum-Likelihood Augmented Discrete Generative Adversarial Networks | Tkach PDF + GaoJi Pdf | |
| Generate | Graphical Generative Adversarial Networks | Arshdeep PDF | |
| Generate | GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models, ICML2018 | Arshdeep PDF | |
| Generate | Inference in probabilistic graphical models by Graph Neural Networks | Arshdeep PDF | |
| Generate | Encoding robust representation for graph generation | Arshdeep PDF | |
| Generate | Junction Tree Variational Autoencoder for Molecular Graph Generation | Tkach PDF + Arshdeep Pdf | |
| Generate | Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation NeurIPS18 | Tkach PDF | |
| Generate | Towards Variational Generation of Small Graphs | Tkach PDF + Arshdeep Pdf | |
| Generate | Convolutional Imputation of Matrix Networks | Tkach PDF | |
| Generate | Graph Convolutional Matrix Completion | Tkach PDF | |
| Generate | NetGAN: Generating Graphs via Random Walks ICML18 | [ULR] | Tkach PDF |
| Beam | Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement | URL | Tkach PDF |
[71]: influence-functions
Table of readings
read on: - 22 Jul 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| GaoJi | A few useful things to know about machine learning | ||
| GaoJi | A few papers related to testing learning, e.g., Understanding Black-box Predictions via Influence Functions | ||
| GaoJi | Automated White-box Testing of Deep Learning Systems 1 | ||
| GaoJi | Testing and Validating Machine Learning Classifiers by Metamorphic Testing 2 | ||
| GaoJi | Software testing: a research travelogue (2000–2014) |
[72]: infomax
Table of readings
read on: - 11 Oct 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | Relational inductive biases, deep learning, and graph networks | ||
| Arshdeep | Discriminative Embeddings of Latent Variable Models for Structured Data | ||
| Jack | Deep Graph Infomax |
read on: - 12 Sep 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Ceyer | A Closer Look at Memorization in Deep Networks, ICML17 1 | ||
| On the Expressive Efficiency of Overlapping Architectures of Deep Learning 2 | DLSSpdf + video | ||
| Mutual Information | Opening the Black Box of Deep Neural Networks via Information 3 | URL + video | |
| ChaoJiang | Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity, NIPS16 |
Table of readings
read on: - 28 Sep 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Jack | Attend, Adapt and Transfer: Attentive Deep Architecture for Adaptive Transfer from multiple sources in the same domain, ICLR17 1 | ||
| Arshdeep | Bidirectional Attention Flow for Machine Comprehension, ICLR17 2 | PDF + code | |
| Ceyer | Image-to-Markup Generation with Coarse-to-Fine Attention, ICML17 | PDF + code | |
| ChaoJiang | Can Active Memory Replace Attention? ; Samy Bengio, NIPS16 3 | ||
| An Information-Theoretic Framework for Fast and Robust Unsupervised Learning via Neural Population Infomax, ICLR17 |
[73]: informax
Table of readings
read on: - 12 Dec 2019
| Team INDEX | Title & Link | Tags | Our Slide |
|---|---|---|---|
| T2 | Empirical Study of Example Forgetting During Deep Neural Network Learning | Sample Selection, forgetting | OurSlide |
| T29 | Select Via Proxy: Efficient Data Selection For Training Deep Networks | Sample Selection | OurSlide |
| T9 | How SGD Selects the Global Minima in over-parameterized Learning | optimization | OurSlide |
| T10 | Escaping Saddles with Stochastic Gradients | optimization | OurSlide |
| T13 | To What Extent Do Different Neural Networks Learn the Same Representation | subspace | OurSlide |
| T19 | On the Information Bottleneck Theory of Deep Learning | informax | OurSlide |
| T20 | Visualizing the Loss Landscape of Neural Nets | normalization | OurSlide |
| T21 | Using Pre-Training Can Improve Model Robustness and Uncertainty | training, analysis | OurSlide |
| T24 | Norm matters: efficient and accurate normalization schemes in deep networks | normalization | OurSlide |
[74]: interpretable
Table of readings
read on: - 05 Aug 2020
| Index | Papers | Our Slides |
|---|---|---|
| 0 | A survey on Interpreting Deep Learning Models | Eli Survey |
| Interpretable Machine Learning: Definitions,Methods, Applications | Arsh Survey | |
| 1 | Explaining Explanations: Axiomatic Feature Interactions for Deep Networks | Arsh Survey |
| 2 | Shapley Value review | Arsh Survey |
| L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data | Bill Survey | |
| Consistent Individualized Feature Attribution for Tree Ensembles | bill Survey | |
| Summary for A value for n-person games | Pan Survey | |
| L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data | Rishab Survey | |
| 3 | Hierarchical Interpretations of Neural Network Predictions | Arsh Survey |
| Hierarchical Interpretations of Neural Network Predictions | Rishab Survey | |
| 4 | Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs | Arsh Survey |
| Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs | Rishab Survey | |
| 5 | Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Models | Rishab Survey |
| Sanchit Survey | ||
| Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection | Sanchit Survey | |
| 6 | This Looks Like That: Deep Learning for Interpretable Image Recognition | Pan Survey |
| 7 | AllenNLP Interpret | Rishab Survey |
| 8 | DISCOVERY OF NATURAL LANGUAGE CONCEPTS IN INDIVIDUAL UNITS OF CNNs | Rishab Survey |
| 9 | How Does BERT Answer Questions? A Layer-Wise Analysis of Transformer Representations | Rishab Survey |
| 10 | Attention is not Explanation | Sanchit Survey |
| Pan Survey | ||
| 11 | Axiomatic Attribution for Deep Networks | Sanchit Survey |
| 12 | Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization | Sanchit Survey |
| 13 | Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifier | Sanchit Survey |
| 14 | “Why Should I Trust You?”Explaining the Predictions of Any Classifier | Yu Survey |
| 15 | INTERPRETATIONS ARE USEFUL: PENALIZING EXPLANATIONS TO ALIGN NEURAL NETWORKS WITH PRIOR KNOWLEDGE | Pan Survey |
read on: - 05 Apr 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Understand | Faithful and Customizable Explanations of Black Box Models | Derrick PDF | |
| Understand | A causal framework for explaining the predictions of black-box sequence-to-sequence models, EMNLP17 | GaoJi PDF + Bill Pdf | |
| Understand | How Powerful are Graph Neural Networks? / Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning | Pdf + Pdf | GaoJi PDF |
| Understand | Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs + GNN Explainer: A Tool for Post-hoc Explanation of Graph Neural Networks | Pdf + PDF | GaoJi PDF |
| Understand | Attention is not Explanation, 2019 | ||
| Understand | Understanding attention in graph neural networks, 2019 |
read on: - 02 Dec 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Jennifer | Adversarial Attacks Against Medical Deep Learning Systems | ||
| Jennifer | Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learning | ||
| Jennifer | Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers | ||
| Jennifer | CleverHans | ||
| Ji | Ji-f18-New papers about adversarial attack |
read on: - 20 Nov 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Bill | Adversarial Examples that Fool both Computer Vision and Time-Limited Humans | ||
| Bill | Adversarial Attacks Against Medical Deep Learning Systems | ||
| Bill | TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing | ||
| Bill | Distilling the Knowledge in a Neural Network | ||
| Bill | Defensive Distillation is Not Robust to Adversarial Examples | ||
| Bill | Adversarial Logit Pairing , Harini Kannan, Alexey Kurakin, Ian Goodfellow |
read on: - 12 May 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Bill | Intriguing Properties of Adversarial Examples, Ekin D. Cubuk, Barret Zoph, Samuel S. Schoenholz, Quoc V. Le 1 | ||
| Bill | Adversarial Spheres 2 | ||
| Bill | Adversarial Transformation Networks: Learning to Generate Adversarial Examples, Shumeet Baluja, Ian Fischer 3 | ||
| Bill | Thermometer encoding: one hot way to resist adversarial examples 4 | ||
| Adversarial Logit Pairing , Harini Kannan, Alexey Kurakin, Ian Goodfellow 5 |
read on: - 10 Oct 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Rita | Learning Important Features Through Propagating Activation Differences, ICML17 1 | ||
| GaoJi | Examples are not Enough, Learn to Criticize! Model Criticism for Interpretable Machine Learning, NIPS16 2 | ||
| Rita | Learning Kernels with Random Features, Aman Sinha*; John Duchi, 3 |
read on: - 21 Sep 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Shijia | Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer, (Dean), ICLR17 1 | ||
| Ceyer | Sequence Modeling via Segmentations, ICML17 2 | ||
| Arshdeep | Input Switched Affine Networks: An RNN Architecture Designed for Interpretability, ICML17 3 |
read on: - 19 Jan 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| AE | Intriguing properties of neural networks / | ||
| AE | Explaining and Harnessing Adversarial Examples | ||
| AE | Towards Deep Learning Models Resistant to Adversarial Attacks | ||
| AE | DeepFool: a simple and accurate method to fool deep neural networks | ||
| AE | Towards Evaluating the Robustness of Neural Networks by Carlini and Wagner | ||
| Data | Basic Survey of ImageNet - LSVRC competition | URL | |
| Understand | Understanding Black-box Predictions via Influence Functions | ||
| Understand | Deep inside convolutional networks: Visualising image classification models and saliency maps | ||
| Understand | BeenKim, Interpretable Machine Learning, ICML17 Tutorial [^1] | ||
| provable | Provable defenses against adversarial examples via the convex outer adversarial polytope, Eric Wong, J. Zico Kolter, | URL |
Table of readings
read on: - 12 Oct 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Jack | A Unified Approach to Interpreting Model Predictions | ||
| Jack | “Why Should I Trust You?”: Explaining the Predictions of Any Classifier | ||
| Jack | Visual Feature Attribution using Wasserstein GANs | ||
| Jack | GAN Dissection: Visualizing and Understanding Generative Adversarial Networks | ||
| GaoJi | Recent Interpretable machine learning papers | ||
| Jennifer | The Building Blocks of Interpretability |
[75]: invariant
Table of readings
read on: - 22 Feb 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| spherical | Spherical CNNs | Fuwen PDF + Arshdeep Pdf | |
| dynamic | Dynamic graph cnn for learning on point clouds, 2018 | Fuwen PDF | |
| basics | Geometric Deep Learning (simple introduction video) | URL | |
| matching | All Graphs Lead to Rome: Learning Geometric and Cycle-Consistent Representations with Graph Convolutional Networks | Fuwen PDF | |
| completion | Geometric matrix completion with recurrent multi-graph neural networks | Fuwen PDF | |
| Tutorial | Geometric Deep Learning on Graphs and Manifolds | URL | Arsh PDF |
| matching | Similarity Learning with Higher-Order Proximity for Brain Network Analysis | Arsh PDF | |
| pairwise | Pixel to Graph with Associative Embedding | Fuwen PDF | |
| 3D | 3D steerable cnns: Learning rotationally equivariant features in volumetric data | URL | Fuwen PDF |
read on: - 25 Jan 2019
| Presenter | Papers | Paper URL | Our Notes |
|---|---|---|---|
| Basics | GraphSAGE: Large-scale Graph Representation Learning by Jure Leskovec Stanford University | URL + PDF | |
| Basics | Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering by Xavier Bresson | URL + PDF | Ryan Pdf |
| Basics | Gated Graph Sequence Neural Networks by Microsoft Research | URL + PDF | Faizan Pdf |
| Basics | DeepWalk - Turning Graphs into Features via Network Embeddings | URL + PDF | |
| Basics | Spectral Networks and Locally Connected Networks on Graphs 1 | GaoJi slides + Bill Pdf | |
| Basics | A Comprehensive Survey on Graph Neural Networks/ Graph Neural Networks: A Review of Methods and Applications | Jack Pdf | |
| GCN | Semi-Supervised Classification with Graph Convolutional Networks | Jack Pdf |
read on: - 21 Jan 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| DeepBind | Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning | ||
| DeepSEA | Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk | ||
| DeepSEA | Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction, ICML 2014 | ||
| BioBasics | A method for integrating and ranking the evidence for biochemical pathways by mining reactions from text, Bioinformatics13 | ||
| BioBasics | Efficient counting of k-mers in DNA sequences using a Bloom filter. Melsted P, Pritchard JK. BMC Bioinformatics. 2011 | ||
| BioBasics | Fast String Kernels using Inexact Matching for Protein Sequence, JMLR 2004 | ||
| BioBasics | NIPS09: Locality-Sensitive Binary Codes from Shift-Invariant Kernels | ||
| MedSignal | Segmenting Time Series: A Survey and Novel Approach, |
[76]: knowledge-graph
Table of readings
read on: - 05 Apr 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Understand | Faithful and Customizable Explanations of Black Box Models | Derrick PDF | |
| Understand | A causal framework for explaining the predictions of black-box sequence-to-sequence models, EMNLP17 | GaoJi PDF + Bill Pdf | |
| Understand | How Powerful are Graph Neural Networks? / Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning | Pdf + Pdf | GaoJi PDF |
| Understand | Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs + GNN Explainer: A Tool for Post-hoc Explanation of Graph Neural Networks | Pdf + PDF | GaoJi PDF |
| Understand | Attention is not Explanation, 2019 | ||
| Understand | Understanding attention in graph neural networks, 2019 |
read on: - 29 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| QA | A Comparison of Current Graph Database Models | Pdf + PDF2 | Bill PDF |
| QA | Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text | Bill [PDF + GaoJi Pdf | |
| QA | Generative Question Answering: Learning to Answer the Whole Question, Mike Lewis, Angela Fan | Bill PDF + GaoJi Pdf | |
| QA | Learning to Reason Science Exam Questions with Contextual Knowledge Graph Embeddings / Knowledge Graph Embedding via Dynamic Mapping Matrix | PDF + Pdf | Bill PDF + GaoJi Pdf |
| Text | Adversarial Text Generation via Feature-Mover’s Distance | URL | Faizan PDF |
| Text | Content preserving text generation with attribute controls | URL | Faizan PDF |
| Text | Multiple-Attribute Text Rewriting, ICLR, 2019, | URL | Faizan PDF |
| Text | Writeprints: a stylometric approach to identity level identification and similarity detection in cyberSpace | URL | Faizan PDF |
[77]: language-processing
Table of readings
read on: - 05 Jun 2020
| Index | Papers | Our Slides |
|---|---|---|
| 1 | Protein 3D Structure Computed from Evolutionary Sequence Variation | Arsh Survey |
| 3 | Regulatory network inference on developmental and evolutionary lineages | Arsh Survey |
| 4 | Deep learning in ultrasound image analysis | Zhe Survey |
| 5 | Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning (DeepBind) | Jack Survey |
| 6 | Canonical and single-cell Hi-C reveal distinct chromatin interaction sub-networks of mammalian transcription factors | Jack Survey |
| 7 | BindSpace decodes transcription factor binding signals by large-scale sequence embedding | Jack Survey |
| 8 | FastXML: A Fast, Accurate and Stable Tree-classifier for eXtreme Multi-label Learning | Jack Survey |
| 9 | Query-Reduction Networks for Question Answering | Bill Survey |
[78]: learn2learn
Table of readings
read on: - 22 Jun 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction 1 | ||
| Arshdeep | Decoupled Neural Interfaces Using Synthetic Gradients 2 | ||
| Arshdeep | Diet Networks: Thin Parameters for Fat Genomics 3 | ||
| Arshdeep | Metric Learning with Adaptive Density Discrimination 4 |
read on: - 02 Jun 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | HyperNetworks, David Ha, Andrew Dai, Quoc V. Le ICLR 2017 1 | ||
| Arshdeep | Learning feed-forward one-shot learners 2 | ||
| Arshdeep | Learning to Learn by gradient descent by gradient descent 3 | ||
| Arshdeep | Dynamic Filter Networks 4 https://arxiv.org/abs/1605.09673 |
[79]: loss
Table of readings
read on: - 11 Dec 2019
| Team INDEX | Title & Link | Tags | Our Slide | |
|---|---|---|---|---|
| T5 | Deep Structured Prediction with Nonlinear Output Transformations | structured | OurSlide | |
| T12 | Large Margin Deep Networks for Classification | OurSlide | large-margin | |
| T15 | Wide Activation for Efficient and Accurate Image Super-Resolution | CNN | OurSlide | |
| T17 | Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks | RNN | OurSlide | |
| T28 | Processing of missing data by neural networks | imputation | OurSlide | |
| T27 | Implicit Acceleration by Overparameterization | analysis | OurSlide |
[80]: low-rank
Table of readings
read on: - 05 Feb 2020
| Index | Papers | Our Slides |
|---|---|---|
| 1 | Beta VAE, Ladder VAE, Causal VAE | Arsh Survey |
| 2 | Learnt Prior VAE | Arsh Survey |
| 3 | Multitask Graph Autoencoder | Arsh Survey |
| 4 | Introduction to component analysi | Zhe Survey |
| 5 | Normalizing flow | Zhe Survey |
| 6 | Nonlinear ICA | Zhe Survey |
| 7 | Deep Convolutional Inverse Graphics Network | Zhe Survey |
read on: - 22 Jun 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction 1 | ||
| Arshdeep | Decoupled Neural Interfaces Using Synthetic Gradients 2 | ||
| Arshdeep | Diet Networks: Thin Parameters for Fat Genomics 3 | ||
| Arshdeep | Metric Learning with Adaptive Density Discrimination 4 |
read on: - 02 Jun 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | HyperNetworks, David Ha, Andrew Dai, Quoc V. Le ICLR 2017 1 | ||
| Arshdeep | Learning feed-forward one-shot learners 2 | ||
| Arshdeep | Learning to Learn by gradient descent by gradient descent 3 | ||
| Arshdeep | Dynamic Filter Networks 4 https://arxiv.org/abs/1605.09673 |
read on: - 20 Jan 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| scalable | Sanjiv Kumar (Columbia EECS 6898), Lecture: Introduction to large-scale machine learning 2010 [^1] | ||
| data scalable | Alex Smola - Berkeley SML: Scalable Machine Learning: Syllabus 2012 [^2] | PDF 2014 + PDF | |
| Binary | Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 | ||
| Model | Binary embeddings with structured hashed projections 1 | ||
| Model | Deep Compression: Compressing Deep Neural Networks (ICLR 2016) 2 |
[81]: manifold
Table of readings
read on: - 22 Feb 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| spherical | Spherical CNNs | Fuwen PDF + Arshdeep Pdf | |
| dynamic | Dynamic graph cnn for learning on point clouds, 2018 | Fuwen PDF | |
| basics | Geometric Deep Learning (simple introduction video) | URL | |
| matching | All Graphs Lead to Rome: Learning Geometric and Cycle-Consistent Representations with Graph Convolutional Networks | Fuwen PDF | |
| completion | Geometric matrix completion with recurrent multi-graph neural networks | Fuwen PDF | |
| Tutorial | Geometric Deep Learning on Graphs and Manifolds | URL | Arsh PDF |
| matching | Similarity Learning with Higher-Order Proximity for Brain Network Analysis | Arsh PDF | |
| pairwise | Pixel to Graph with Associative Embedding | Fuwen PDF | |
| 3D | 3D steerable cnns: Learning rotationally equivariant features in volumetric data | URL | Fuwen PDF |
[82]: markov
Table of readings
read on: - 05 Jan 2020
| Index | Papers | Our Slides |
|---|---|---|
| 1 | A Flexible Generative Framework for Graph-based Semi-supervised Learning | Arsh Survey |
| 2 | Learning Discrete Structures for Graph Neural Networks | Arsh Survey |
| 4 | Graph Markov Neural Nets | Arsh Survey |
| Graph Markov Neural Networks | Jack Survey | |
| 5 | GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations | Arsh Survey |
| 6 | Subgraph Neural Networks | Arsh Survey |
| 7 | Pointer Graph Networks | Arsh Survey |
| 8 | Modeling Relational Data with Graph Convolutional Networks | Arsh Survey |
| 9 | Graph Learning | Zhe Survey |
| 8 | Neural Relational Inference | Zhe Survey |
[83]: matching
Table of readings
read on: - 22 Feb 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| spherical | Spherical CNNs | Fuwen PDF + Arshdeep Pdf | |
| dynamic | Dynamic graph cnn for learning on point clouds, 2018 | Fuwen PDF | |
| basics | Geometric Deep Learning (simple introduction video) | URL | |
| matching | All Graphs Lead to Rome: Learning Geometric and Cycle-Consistent Representations with Graph Convolutional Networks | Fuwen PDF | |
| completion | Geometric matrix completion with recurrent multi-graph neural networks | Fuwen PDF | |
| Tutorial | Geometric Deep Learning on Graphs and Manifolds | URL | Arsh PDF |
| matching | Similarity Learning with Higher-Order Proximity for Brain Network Analysis | Arsh PDF | |
| pairwise | Pixel to Graph with Associative Embedding | Fuwen PDF | |
| 3D | 3D steerable cnns: Learning rotationally equivariant features in volumetric data | URL | Fuwen PDF |
read on: - 01 Feb 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Matching | Deep Learning of Graph Matching, | PDF+ PDF | Jack Pdf |
| Matching | Graph Edit Distance Computation via Graph Neural Networks | Jack Pdf | |
| Basics | Link Prediction Based on Graph Neural Networks | Jack Pdf | |
| Basics | Supervised Community Detection with Line Graph Neural Networks | Jack Pdf | |
| Basics | Graph mining: Laws, generators, and algorithms | Arshdeep PDF | |
| pooling | Hierarchical graph representation learning with differentiable pooling | Eamon PDF |
[84]: matching-net
Table of readings
read on: - 18 Jan 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| seq2seq | Sequence to Sequence Learning with Neural Networks | ||
| Set | Pointer Networks | ||
| Set | Order Matters: Sequence to Sequence for Sets | ||
| Point Attention | Multiple Object Recognition with Visual Attention | ||
| Memory | End-To-End Memory Networks | Jack Survey | |
| Memory | Neural Turing Machines | ||
| Memory | Hybrid computing using a neural network with dynamic external memory | ||
| Muthu | Matching Networks for One Shot Learning (NIPS16) 1 | ||
| Jack | Meta-Learning with Memory-Augmented Neural Networks (ICML16) 2 | ||
| Metric | ICML07 Best Paper - Information-Theoretic Metric Learning |
[85]: matrix-completion
Table of readings
read on: - 15 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Generate | Maximum-Likelihood Augmented Discrete Generative Adversarial Networks | Tkach PDF + GaoJi Pdf | |
| Generate | Graphical Generative Adversarial Networks | Arshdeep PDF | |
| Generate | GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models, ICML2018 | Arshdeep PDF | |
| Generate | Inference in probabilistic graphical models by Graph Neural Networks | Arshdeep PDF | |
| Generate | Encoding robust representation for graph generation | Arshdeep PDF | |
| Generate | Junction Tree Variational Autoencoder for Molecular Graph Generation | Tkach PDF + Arshdeep Pdf | |
| Generate | Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation NeurIPS18 | Tkach PDF | |
| Generate | Towards Variational Generation of Small Graphs | Tkach PDF + Arshdeep Pdf | |
| Generate | Convolutional Imputation of Matrix Networks | Tkach PDF | |
| Generate | Graph Convolutional Matrix Completion | Tkach PDF | |
| Generate | NetGAN: Generating Graphs via Random Walks ICML18 | [ULR] | Tkach PDF |
| Beam | Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement | URL | Tkach PDF |
[86]: memorization
Table of readings
read on: - 12 Sep 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Ceyer | A Closer Look at Memorization in Deep Networks, ICML17 1 | ||
| On the Expressive Efficiency of Overlapping Architectures of Deep Learning 2 | DLSSpdf + video | ||
| Mutual Information | Opening the Black Box of Deep Neural Networks via Information 3 | URL + video | |
| ChaoJiang | Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity, NIPS16 |
[87]: memory
Table of readings
read on: - 03 Oct 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Tianlu | Ask Me Anything: Dynamic Memory Networks for Natural Language Processing, ICML17 1 | PDF + code | |
| Jack | Reasoning with Memory Augmented Neural Networks for Language Comprehension, ICLR17 2 | ||
| Xueying | State-Frequency Memory Recurrent Neural Networks, ICML17 3 |
read on: - 28 Sep 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Jack | Attend, Adapt and Transfer: Attentive Deep Architecture for Adaptive Transfer from multiple sources in the same domain, ICLR17 1 | ||
| Arshdeep | Bidirectional Attention Flow for Machine Comprehension, ICLR17 2 | PDF + code | |
| Ceyer | Image-to-Markup Generation with Coarse-to-Fine Attention, ICML17 | PDF + code | |
| ChaoJiang | Can Active Memory Replace Attention? ; Samy Bengio, NIPS16 3 | ||
| An Information-Theoretic Framework for Fast and Robust Unsupervised Learning via Neural Population Infomax, ICLR17 |
read on: - 07 Sep 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Beilun | Learning Deep Parsimonious Representations, NIPS16 1 | ||
| Jack | Dense Associative Memory for Pattern Recognition, NIPS16 2 | PDF + video |
read on: - 18 Jan 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| seq2seq | Sequence to Sequence Learning with Neural Networks | ||
| Set | Pointer Networks | ||
| Set | Order Matters: Sequence to Sequence for Sets | ||
| Point Attention | Multiple Object Recognition with Visual Attention | ||
| Memory | End-To-End Memory Networks | Jack Survey | |
| Memory | Neural Turing Machines | ||
| Memory | Hybrid computing using a neural network with dynamic external memory | ||
| Muthu | Matching Networks for One Shot Learning (NIPS16) 1 | ||
| Jack | Meta-Learning with Memory-Augmented Neural Networks (ICML16) 2 | ||
| Metric | ICML07 Best Paper - Information-Theoretic Metric Learning |
[88]: meta-learning
Table of readings
read on: - 18 Jan 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| seq2seq | Sequence to Sequence Learning with Neural Networks | ||
| Set | Pointer Networks | ||
| Set | Order Matters: Sequence to Sequence for Sets | ||
| Point Attention | Multiple Object Recognition with Visual Attention | ||
| Memory | End-To-End Memory Networks | Jack Survey | |
| Memory | Neural Turing Machines | ||
| Memory | Hybrid computing using a neural network with dynamic external memory | ||
| Muthu | Matching Networks for One Shot Learning (NIPS16) 1 | ||
| Jack | Meta-Learning with Memory-Augmented Neural Networks (ICML16) 2 | ||
| Metric | ICML07 Best Paper - Information-Theoretic Metric Learning |
[89]: metamorphic
Table of readings
read on: - 22 Jul 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| GaoJi | A few useful things to know about machine learning | ||
| GaoJi | A few papers related to testing learning, e.g., Understanding Black-box Predictions via Influence Functions | ||
| GaoJi | Automated White-box Testing of Deep Learning Systems 1 | ||
| GaoJi | Testing and Validating Machine Learning Classifiers by Metamorphic Testing 2 | ||
| GaoJi | Software testing: a research travelogue (2000–2014) |
[90]: metric-learning
Table of readings
read on: - 27 Aug 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Derrick | GloVe: Global Vectors for Word Representation | ||
| Derrick | PARL.AI: A unified platform for sharing, training and evaluating dialog models across many tasks. | URL | |
| Derrick | scalable nearest neighbor algorithms for high dimensional data (PAMI14) 1 | ||
| Derrick | StarSpace: Embed All The Things! | ||
| Derrick | Weaver: Deep Co-Encoding of Questions and Documents for Machine Reading, Martin Raison, Pierre-Emmanuel Mazaré, Rajarshi Das, Antoine Bordes |
read on: - 18 Jan 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| seq2seq | Sequence to Sequence Learning with Neural Networks | ||
| Set | Pointer Networks | ||
| Set | Order Matters: Sequence to Sequence for Sets | ||
| Point Attention | Multiple Object Recognition with Visual Attention | ||
| Memory | End-To-End Memory Networks | Jack Survey | |
| Memory | Neural Turing Machines | ||
| Memory | Hybrid computing using a neural network with dynamic external memory | ||
| Muthu | Matching Networks for One Shot Learning (NIPS16) 1 | ||
| Jack | Meta-Learning with Memory-Augmented Neural Networks (ICML16) 2 | ||
| Metric | ICML07 Best Paper - Information-Theoretic Metric Learning |
[91]: mimic
Table of readings
read on: - 22 Apr 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Muthu | Optimization Methods for Large-Scale Machine Learning, Léon Bottou, Frank E. Curtis, Jorge Nocedal 1 | ||
| Muthu | Fast Training of Recurrent Networks Based on EM Algorithm (1998) 2 | ||
| Muthu | FitNets: Hints for Thin Deep Nets, ICLR15 3 | ||
| Muthu | Two NIPS 2015 Deep Learning Optimization Papers | ||
| Muthu | Difference Target Propagation (2015) 4 |
[92]: mobile
Table of readings
read on: - 25 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Edge | MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications | ||
| Edge | XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks | URL | Ryan PDF |
| Edge | DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices | Eamon PDF | |
| Edge | Loss-aware Binarization of Deep Networks, ICLR17 | Ryan PDF | |
| Edge | Espresso: Efficient Forward Propagation for Binary Deep Neural Networks | Eamon PDF | |
| Dynamic | Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution | Weilin PDF | |
| Dynamic | Dynamic Scheduling For Dynamic Control Flow in Deep Learning Systems | ||
| Dynamic | Cavs: An Efficient Runtime System for Dynamic Neural Networks |
[93]: model-as-sample
Table of readings
read on: - 10 Dec 2019
| Team INDEX | Title & Link | Tags | Our Slide |
|---|---|---|---|
| T3 | Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints | submodular, coreset, safety | OurSlide |
| T6 | Decision Boundary Analysis of Adversarial Examples | adversarial-examples | OurSlide |
| T8 | Robustness may be at odds with accuracy | robustness | OurSlide |
| T18 | Towards Reverse-Engineering Black-Box Neural Networks | meta, model-as-sample, safety, privacy | OurSlide |
| T23 | The Odds are Odd: A Statistical Test for Detecting Adversarial Examples | adversarial-examples | OurSlide |
| T25 | Learning how to explain neural networks: PatternNet and PatternAttribution | Attribution, Interpretable | OurSlide |
| T31 | Detecting Statistical Interactions from Neural Network Weights | Interpretable, Relational | OurSlide |
[94]: model-criticism
Table of readings
read on: - 16 Nov 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | Generalization and Equilibrium in Generative Adversarial Nets (ICML17) 1 | PDF + video | |
| Arshdeep | Mode Regularized Generative Adversarial Networks (ICLR17) 2 | ||
| Bargav | Improving Generative Adversarial Networks with Denoising Feature Matching, ICLR17 3 | ||
| Anant | Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy, ICLR17 4 | PDF + code |
read on: - 10 Oct 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Rita | Learning Important Features Through Propagating Activation Differences, ICML17 1 | ||
| GaoJi | Examples are not Enough, Learn to Criticize! Model Criticism for Interpretable Machine Learning, NIPS16 2 | ||
| Rita | Learning Kernels with Random Features, Aman Sinha*; John Duchi, 3 |
[95]: molecule
Table of readings
read on: - 15 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Generate | Maximum-Likelihood Augmented Discrete Generative Adversarial Networks | Tkach PDF + GaoJi Pdf | |
| Generate | Graphical Generative Adversarial Networks | Arshdeep PDF | |
| Generate | GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models, ICML2018 | Arshdeep PDF | |
| Generate | Inference in probabilistic graphical models by Graph Neural Networks | Arshdeep PDF | |
| Generate | Encoding robust representation for graph generation | Arshdeep PDF | |
| Generate | Junction Tree Variational Autoencoder for Molecular Graph Generation | Tkach PDF + Arshdeep Pdf | |
| Generate | Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation NeurIPS18 | Tkach PDF | |
| Generate | Towards Variational Generation of Small Graphs | Tkach PDF + Arshdeep Pdf | |
| Generate | Convolutional Imputation of Matrix Networks | Tkach PDF | |
| Generate | Graph Convolutional Matrix Completion | Tkach PDF | |
| Generate | NetGAN: Generating Graphs via Random Walks ICML18 | [ULR] | Tkach PDF |
| Beam | Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement | URL | Tkach PDF |
read on: - 15 Feb 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Bio | KDEEP: Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks, 2018 1 | Eli Pdf | |
| Bio | Molecular geometry prediction using a deep generative graph neural network | Eli Pdf | |
| Bio | Visualizing convolutional neural network protein-ligand scoring | PDF() | Eli PDF |
| Bio | Deep generative models of genetic variation capture mutation effects | PDF() | Eli PDF |
| Bio | Attentive cross-modal paratope prediction | Eli PDF |
read on: - 21 Dec 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | Constrained Graph Variational Autoencoders for Molecule Design | ||
| Arshdeep | Learning Deep Generative Models of Graphs | ||
| Arshdeep | Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation | ||
| Jack | Generating and designing DNA with deep generative models |
read on: - 16 Oct 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Eric | Modeling polypharmacy side effects with graph convolutional networks | ||
| Eric | Protein Interface Prediction using Graph Convolutional Networks | ||
| Eric | Structure biology meets data science: does anything change | URL | |
| Eric | DeepSite: protein-binding site predictor using 3D-convolutional neural networks | URL |
[96]: multi-label
Table of readings
read on: - 11 May 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Chao | Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification | ||
| Jack | FastXML: A Fast, Accurate and Stable Tree-classifier for eXtreme Multi-label Learning | ||
| BasicMLC | Multi-Label Classification: An Overview | ||
| SPEN | Structured Prediction Energy Networks | ||
| InfNet | Learning Approximate Inference Networks for Structured Prediction | ||
| SPENMLC | Deep Value Networks | ||
| Adversarial | Semantic Segmentation using Adversarial Networks | ||
| EmbedMLC | StarSpace: Embed All The Things! | ||
| deepMLC | CNN-RNN: A Unified Framework for Multi-label Image Classification/ CVPR 2016 | ||
| deepMLC | Order-Free RNN with Visual Attention for Multi-Label Classification / AAAI 2018 |
[97]: multi-task
Table of readings
read on: - 05 Apr 2020
| Index | Papers | Our Slides |
|---|---|---|
| 1 | Invariant Risk Minimization | Zhe Survey |
| 2 | Causal Machine Learning | Zhe Survey |
| 3 | A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms | Zhe Survey |
| 3 | Review on Optimization-Based Meta Learning | Zhe Survey |
| 4 | Domain adaptation and counterfactual prediction | Zhe Survey |
| 5 | Gaussian Processes | Zhe Survey |
| 6 | A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data | Zhe Survey |
| 7 | Few-shot domain adaptation by causal mechanism transfer | Zhe Survey |
read on: - 21 Nov 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Jack | Hasselt - Deep Reinforcement Learning | RLSS17.pdf + video | |
| Tianlu | Roux - RL in the Industry | RLSS17.pdf + video | PDF / PDF-Bandit |
| Xueying | Singh - Steps Towards Continual Learning | pdf + video | |
| GaoJi | Distral: Robust Multitask Reinforcement Learning 1 |
Table of readings
read on: - 07 Dec 2019
| Team INDEX | Title & Link | Tags | Our Slide |
|---|---|---|---|
| T11 | Parameter-Efficient Transfer Learning for NLP | meta, BERT, text, Transfer | OurSlide |
| T22 | Deep Asymmetric Multi-task Feature Learning | meta, regularization, Multi-task | OurSlide |
[98]: mutual-information
Table of readings
read on: - 05 May 2020
| Index | Papers | Our Slides |
|---|---|---|
| 1 | Review on Semi-Supervised Learning | Zhe Survey |
| 2 | Review on Generative Adversarial Networks | Zhe Survey |
| 3 | Information theory in deep learning | Zhe Survey |
| 4 | Lagrange Optimization | Zhe Survey |
| 5 | Deep Learning and Information Theory, and Graph Neural Network | Derrick Survey |
| 6 | Loss Functions for Deep Structured Models | Jack Survey |
| 7 | Group Sparsity and Optimization | Zhe Survey |
[99]: neural-programming
Table of readings
read on: - 17 Oct 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Jack | Learning to Query, Reason, and Answer Questions On Ambiguous Texts, ICLR17 1 | ||
| Arshdeep | Making Neural Programming Architectures Generalize via Recursion, ICLR17 2 | ||
| Xueying | Towards Deep Interpretability (MUS-ROVER II): Learning Hierarchical Representations of Tonal Music, ICLR17 3 |
[100]: neuroscience
Table of readings
[101]: nlp
Table of readings
read on: - 29 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| QA | A Comparison of Current Graph Database Models | Pdf + PDF2 | Bill PDF |
| QA | Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text | Bill [PDF + GaoJi Pdf | |
| QA | Generative Question Answering: Learning to Answer the Whole Question, Mike Lewis, Angela Fan | Bill PDF + GaoJi Pdf | |
| QA | Learning to Reason Science Exam Questions with Contextual Knowledge Graph Embeddings / Knowledge Graph Embedding via Dynamic Mapping Matrix | PDF + Pdf | Bill PDF + GaoJi Pdf |
| Text | Adversarial Text Generation via Feature-Mover’s Distance | URL | Faizan PDF |
| Text | Content preserving text generation with attribute controls | URL | Faizan PDF |
| Text | Multiple-Attribute Text Rewriting, ICLR, 2019, | URL | Faizan PDF |
| Text | Writeprints: a stylometric approach to identity level identification and similarity detection in cyberSpace | URL | Faizan PDF |
read on: - 22 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Scalable | FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling | Ryan PDF + Arshdeep Pdf | |
| Scalable | MILE: A Multi-Level Framework for Scalable Graph Embedding | Ryan PDF | |
| Scalable | LanczosNet: Multi-Scale Deep Graph Convolutional Networks | Ryan PDF | |
| Scalable | Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis | Derrick PDF | |
| Scalable | Towards Federated learning at Scale: System Design | URL | Derrick PDF |
| Scalable | DNN Dataflow Choice Is Overrated | Derrick PDF | |
| Scalable | Towards Efficient Large-Scale Graph Neural Network Computing | Derrick PDF | |
| Scalable | PyTorch Geometric | URL | |
| Scalable | PyTorch BigGraph | URL | |
| Scalable | Simplifying Graph Convolutional Networks | ||
| Scalable | Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks |
read on: - 15 Mar 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Generate | Maximum-Likelihood Augmented Discrete Generative Adversarial Networks | Tkach PDF + GaoJi Pdf | |
| Generate | Graphical Generative Adversarial Networks | Arshdeep PDF | |
| Generate | GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models, ICML2018 | Arshdeep PDF | |
| Generate | Inference in probabilistic graphical models by Graph Neural Networks | Arshdeep PDF | |
| Generate | Encoding robust representation for graph generation | Arshdeep PDF | |
| Generate | Junction Tree Variational Autoencoder for Molecular Graph Generation | Tkach PDF + Arshdeep Pdf | |
| Generate | Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation NeurIPS18 | Tkach PDF | |
| Generate | Towards Variational Generation of Small Graphs | Tkach PDF + Arshdeep Pdf | |
| Generate | Convolutional Imputation of Matrix Networks | Tkach PDF | |
| Generate | Graph Convolutional Matrix Completion | Tkach PDF | |
| Generate | NetGAN: Generating Graphs via Random Walks ICML18 | [ULR] | Tkach PDF |
| Beam | Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement | URL | Tkach PDF |
read on: - 02 Dec 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Jennifer | Adversarial Attacks Against Medical Deep Learning Systems | ||
| Jennifer | Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learning | ||
| Jennifer | Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers | ||
| Jennifer | CleverHans | ||
| Ji | Ji-f18-New papers about adversarial attack |
read on: - 10 Jan 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Bill | Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation 1 | ||
| Bill | Measuring the tendency of CNNs to Learn Surface Statistical Regularities Jason Jo, Yoshua Bengio | ||
| Bill | Generating Sentences by Editing Prototypes, Kelvin Guu, Tatsunori B. Hashimoto, Yonatan Oren, Percy Liang 2 | ||
| Bill | On the importance of single directions for generalization, Ari S. Morcos, David G.T. Barrett, Neil C. Rabinowitz, Matthew Botvinick |
read on: - 10 Jan 2018
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Bill | Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation 1 | ||
| Bill | Measuring the tendency of CNNs to Learn Surface Statistical Regularities Jason Jo, Yoshua Bengio | ||
| Bill | Generating Sentences by Editing Prototypes, Kelvin Guu, Tatsunori B. Hashimoto, Yonatan Oren, Percy Liang 2 | ||
| Bill | On the importance of single directions for generalization, Ari S. Morcos, David G.T. Barrett, Neil C. Rabinowitz, Matthew Botvinick |
read on: - 12 Jan 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| NLP | A Neural Probabilistic Language Model | ||
| Text | Bag of Tricks for Efficient Text Classification | ||
| Text | Character-level Convolutional Networks for Text Classification | ||
| NLP | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | ||
| seq2seq | Neural Machine Translation by Jointly Learning to Align and Translate | ||
| NLP | Natural Language Processing (almost) from Scratch | ||
| Train | Curriculum learning | ||
| Muthu | NeuroIPS Embedding Papers survey 2012 to 2015 | NIPS | |
| Basics | Efficient BackProp |
[102]: noise
Table of readings
read on: - 05 Apr 2019
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Understand | Faithful and Customizable Explanations of Black Box Models | Derrick PDF | |
| Understand | A causal framework for explaining the predictions of black-box sequence-to-sequence models, EMNLP17 | GaoJi PDF + Bill Pdf | |
| Understand | How Powerful are Graph Neural Networks? / Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning | Pdf + Pdf | GaoJi PDF |
| Understand | Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs + GNN Explainer: A Tool for Post-hoc Explanation of Graph Neural Networks | Pdf + PDF | GaoJi PDF |
| Understand | Attention is not Explanation, 2019 | ||
| Understand | Understanding attention in graph neural networks, 2019 |
read on: - 26 Oct 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Tianlu | Robustness of classifiers: from adversarial to random noise, NIPS16 | PDF 1 | |
| Anant | Blind Attacks on Machine Learners, 2 NIPS16 | ||
| Data Noising as Smoothing in Neural Network Language Models (Ng), ICLR17 3 | |||
| The Robustness of Estimator Composition, NIPS16 4 |
read on: - 17 Oct 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Jack | Learning to Query, Reason, and Answer Questions On Ambiguous Texts, ICLR17 1 | ||
| Arshdeep | Making Neural Programming Architectures Generalize via Recursion, ICLR17 2 | ||
| Xueying | Towards Deep Interpretability (MUS-ROVER II): Learning Hierarchical Representations of Tonal Music, ICLR17 3 |
[103]: nonparametric
Table of readings
read on: - 21 Sep 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Shijia | Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer, (Dean), ICLR17 1 | ||
| Ceyer | Sequence Modeling via Segmentations, ICML17 2 | ||
| Arshdeep | Input Switched Affine Networks: An RNN Architecture Designed for Interpretability, ICML17 3 |
read on: - 19 Sep 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Jack | Learning End-to-End Goal-Oriented Dialog, ICLR17 1 | ||
| Bargav | Nonparametric Neural Networks, ICLR17 2 | ||
| Bargav | Learning Structured Sparsity in Deep Neural Networks, NIPS16 3 | ||
| Arshdeep | Learning the Number of Neurons in Deep Networks, NIPS16 4 |
[104]: normalization
Table of readings
read on: - 12 Dec 2019
| Team INDEX | Title & Link | Tags | Our Slide |
|---|---|---|---|
| T2 | Empirical Study of Example Forgetting During Deep Neural Network Learning | Sample Selection, forgetting | OurSlide |
| T29 | Select Via Proxy: Efficient Data Selection For Training Deep Networks | Sample Selection | OurSlide |
| T9 | How SGD Selects the Global Minima in over-parameterized Learning | optimization | OurSlide |
| T10 | Escaping Saddles with Stochastic Gradients | optimization | OurSlide |
| T13 | To What Extent Do Different Neural Networks Learn the Same Representation | subspace | OurSlide |
| T19 | On the Information Bottleneck Theory of Deep Learning | informax | OurSlide |
| T20 | Visualizing the Loss Landscape of Neural Nets | normalization | OurSlide |
| T21 | Using Pre-Training Can Improve Model Robustness and Uncertainty | training, analysis | OurSlide |
| T24 | Norm matters: efficient and accurate normalization schemes in deep networks | normalization | OurSlide |
[105]: ntm
Table of readings
read on: - 18 Jan 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| seq2seq | Sequence to Sequence Learning with Neural Networks | ||
| Set | Pointer Networks | ||
| Set | Order Matters: Sequence to Sequence for Sets | ||
| Point Attention | Multiple Object Recognition with Visual Attention | ||
| Memory | End-To-End Memory Networks | Jack Survey | |
| Memory | Neural Turing Machines | ||
| Memory | Hybrid computing using a neural network with dynamic external memory | ||
| Muthu | Matching Networks for One Shot Learning (NIPS16) 1 | ||
| Jack | Meta-Learning with Memory-Augmented Neural Networks (ICML16) 2 | ||
| Metric | ICML07 Best Paper - Information-Theoretic Metric Learning |
[106]: optimization
Table of readings
read on: - 09 Nov 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Shijia | Professor Forcing: A New Algorithm for Training Recurrent Networks, 1 NIPS16 | PDF + Video | |
| Beilun+Arshdeep | Mollifying Networks, Bengio, ICLR17 2 | PDF / PDF2 |
read on: - 07 Nov 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| GaoJi | Forward and Reverse Gradient-Based Hyperparameter Optimization, ICML17 1 | ||
| Chaojiang | Adaptive Neural Networks for Efficient Inference, ICML17 2 | ||
| Bargav | Practical Gauss-Newton Optimisation for Deep Learning, ICML17 3 | ||
| Rita | How to Escape Saddle Points Efficiently, ICML17 4 | ||
| Batched High-dimensional Bayesian Optimization via Structural Kernel Learning |
read on: - 02 Nov 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| GaoJi | Neural Architecture Search with Reinforcement Learning, ICLR17 1 | ||
| Ceyer | Learning to learn 2 | DLSS17video | |
| Beilun | Optimization as a Model for Few-Shot Learning, ICLR17 3 | PDF + More | |
| Anant | Neural Optimizer Search with Reinforcement Learning, ICML17 4 |
Table of readings
read on: - 05 May 2020
| Index | Papers | Our Slides |
|---|---|---|
| 1 | Review on Semi-Supervised Learning | Zhe Survey |
| 2 | Review on Generative Adversarial Networks | Zhe Survey |
| 3 | Information theory in deep learning | Zhe Survey |
| 4 | Lagrange Optimization | Zhe Survey |
| 5 | Deep Learning and Information Theory, and Graph Neural Network | Derrick Survey |
| 6 | Loss Functions for Deep Structured Models | Jack Survey |
| 7 | Group Sparsity and Optimization | Zhe Survey |
read on: - 12 Dec 2019
| Team INDEX | Title & Link | Tags | Our Slide |
|---|---|---|---|
| T2 | Empirical Study of Example Forgetting During Deep Neural Network Learning | Sample Selection, forgetting | OurSlide |
| T29 | Select Via Proxy: Efficient Data Selection For Training Deep Networks | Sample Selection | OurSlide |
| T9 | How SGD Selects the Global Minima in over-parameterized Learning | optimization | OurSlide |
| T10 | Escaping Saddles with Stochastic Gradients | optimization | OurSlide |
| T13 | To What Extent Do Different Neural Networks Learn the Same Representation | subspace | OurSlide |
| T19 | On the Information Bottleneck Theory of Deep Learning | informax | OurSlide |
| T20 | Visualizing the Loss Landscape of Neural Nets | normalization | OurSlide |
| T21 | Using Pre-Training Can Improve Model Robustness and Uncertainty | training, analysis | OurSlide |
| T24 | Norm matters: efficient and accurate normalization schemes in deep networks | normalization | OurSlide |
read on: - 31 Oct 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Ceyer | An overview of gradient optimization algorithms, 1 | ||
| Shijia | Osborne - Probabilistic numerics for deep learning 2 | DLSS 2017 + Video | PDF / PDF2 |
| Jack | Automated Curriculum Learning for Neural Networks, ICML17 3 | ||
| DLSS17 | Johnson - Automatic Differentiation 4 | slide + video |
read on: - 22 Jun 2017
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Arshdeep | Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction 1 |