Deep Learning Readings Organized by Tags

Click on a tag to see relevant list of readings.


0Survey

No. Date Title and Information PaperYear
1 2018, Feb, 20 Survey18- My Survey Talk at UVA HMI Seminar - A quick and rough overview of DNN 2018-me
2 2018, Aug, 29 Survey18- My Tutorial Talk at ACM BCB18 - Interpretable Deep Learning 2018-me

Survey18- My Survey Talk at UVA HMI Seminar - A quick and rough overview of DNN

Presenter Papers Information OurPresentation
Dr. Qi A quick and rough survey of Deep-Neural-Networks   PDF
       

Survey18- My Tutorial Talk at ACM BCB18 - Interpretable Deep Learning

Presenter Papers Information OurPresentation
Dr. Qi Making Deep Learning Understandable for Analyzing Sequential Data about Gene Regulation   PDF
       


1Foundations

No. Date Title and Information PaperYear
1 2017, Aug, 22 Foundations I -Andrew Ng - Nuts and Bolts of Applying Deep Learning 2017-W1
2 2017, Aug, 24 Foundations II - Ganguli - Theoretical Neuroscience and Deep Learning DLSS16 2017-W1
3 2017, Sep, 5 Foundations III - Investigating Behaviors of DNN 2017-W3
4 2017, Sep, 7 Foundations IV - Investigating Behaviors of DNN 2017-W3
5 2017, Sep, 12 Foundations V - More about Behaviors of DNN 2017-W4
6 2017, Sep, 14 Foundations VI - More about Behaviors of DNN 2017-W4

Foundations I -Andrew Ng - Nuts and Bolts of Applying Deep Learning

NIPS16 Andrew Ng - Nuts and Bolts of Applying Deep Learning: video
DLSS17 Doina Precup - Machine Learning - Bayesian Views (56:50m to 1:04:45 slides) video + slide

Foundations II - Ganguli - Theoretical Neuroscience and Deep Learning DLSS16

Ganguli - Theoretical Neuroscience and Deep Learning

DLSS16 video
DLSS17 video + slide

Foundations III - Investigating Behaviors of DNN

Presenter Papers Information OurPresentation
Rita On the Expressive Power of Deep Neural Networks PDF PDF
Arshdeep Understanding deep learning requires rethinking generalization, ICLR17 PDF PDF
Tianlu On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima, ICLR17 PDF PDF

Foundations IV - Investigating Behaviors of DNN

Presenter Papers Information OurPresentation
Rita Learning Kernels with Random Features, Aman Sinha*; John Duchi, PDF PDF
Beilun Learning Deep Parsimonious Representations, NIPS16 PDF PDF
Jack Dense Associative Memory for Pattern Recognition, NIPS16 PDF + video PDF
  On the Expressive Efficiency of Overlapping Architectures of Deep Learning DLSSpdf + video  

Foundations V - More about Behaviors of DNN

Presenter Papers Information OurPresentation
Tianlu Large-Scale Evolution of Image Classifiers, ICML17 PDF PDF
Ceyer A Closer Look at Memorization in Deep Networks, ICML17 PDF PDF
Bargav Learning Structured Sparsity in Deep Neural Networks, NIPS16 PDF PDF
Arshdeep Learning the Number of Neurons in Deep Networks, NIPS16 PDF PDF

Foundations VI - More about Behaviors of DNN

Presenter Papers Information OurPresentation
SE Equivariance Through Parameter-Sharing, ICML17 PDF  
SE Why Deep Neural Networks for Function Approximation?, ICLR17 PDF  
SE Geometry of Neural Network Loss Surfaces via Random Matrix Theory, ICML17 PDF  
SE Deep learning in the brain DLSS17 + Video  


2Structures

No. Date Title and Information PaperYear
1 2017, Jan, 22 Structures17- Memory-Augmented Networks 2017-team
2 2017, Mar, 2 Structures17 -Adaptive Deep Networks I 2017-team
3 2017, Mar, 22 Structures17- DNN based Embedding 2017-team
4 2017, Jun, 22 Structures17 - Adaptive Deep Networks II 2017-team
5 2017, Sep, 19 Structure I - Varying DNN structures for an application 2017-W5
6 2017, Sep, 21 Structure II - DNN with Varying Structures 2017-W5
7 2017, Sep, 26 Structure III - DNN with Attention 2017-W6
8 2017, Sep, 28 Structure IV - DNN with Attention 2 2017-W6
9 2017, Oct, 3 Structure V - DNN with Memory 2017-W7
10 2017, Oct, 5 Structure VI - DNN with Adaptive Structures 2017-W7
11 2018, May, 3 Structures18- More Attention in DNN 2018-team
12 2018, Jul, 27 Application18- A few DNN for Question Answering 2018-team
13 2018, Aug, 1 Structures18- DNN for Relations 2018-team

Structures17- Memory-Augmented Networks

Presenter Papers Information OurPresentation
Muthu Matching Networks for One Shot Learning (NIPS16) PDF PDF
Jack Meta-Learning with Memory-Augmented Neural Networks (ICML16) PDF PDF

Structures17 -Adaptive Deep Networks I

Presenter Papers Information OurPresentation
Arshdeep HyperNetworks, David Ha, Andrew Dai, Quoc V. Le ICLR 2017 PDF PDF
Arshdeep Learning feed-forward one-shot learners PDF PDF
Arshdeep Learning to Learn by gradient descent by gradient descent PDF PDF
Arshdeep https://arxiv.org/abs/1605.09673 PDF PDF

Structures17- DNN based Embedding

Presenter Papers Information OurPresentation
Muthu NIPS Embedding Papers survey 2012 to 2015 NIPS PDF
Tobin Binary embeddings with structured hashed projections PDF PDF

Structures17 - Adaptive Deep Networks II

Presenter Papers Information OurPresentation
Arshdeep Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction PDF PDF
Arshdeep Decoupled Neural Interfaces Using Synthetic Gradients PDF PDF
Arshdeep Diet Networks: Thin Parameters for Fat Genomics PDF PDF
Arshdeep Metric Learning with Adaptive Density Discrimination PDF PDF

Structure I - Varying DNN structures for an application

Presenter Papers Information OurPresentation
Jack Learning End-to-End Goal-Oriented Dialog, ICLR17 PDF PDF
Arshdeep Making Neural Programming Architectures Generalize via Recursion, ICLR17 PDF PDF
Bargav Nonparametric Neural Networks, ICLR17 PDF PDF

Structure II - DNN with Varying Structures

Presenter Papers Information OurPresentation
Shijia Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer, (Dean), ICLR17 PDF PDF
Xueying Towards Deep Interpretability (MUS-ROVER II): Learning Hierarchical Representations of Tonal Music, ICLR17 PDF PDF
Ceyer Sequence Modeling via Segmentations, ICML17 PDF PDF
Arshdeep Input Switched Affine Networks: An RNN Architecture Designed for Interpretability, ICML17 PDF PDF

Structure III - DNN with Attention

Presenter Papers Information OurPresentation
Rita Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer, ICLR17 PDF PDF
Tianlu Dynamic Coattention Networks For Question Answering, ICLR17 PDF + code PDF
ChaoJiang Structured Attention Networks, ICLR17 PDF + code PDF

Structure IV - DNN with Attention 2

Presenter Papers Information OurPresentation
Jack Attend, Adapt and Transfer: Attentive Deep Architecture for Adaptive Transfer from multiple sources in the same domain, ICLR17 PDF PDF
Arshdeep Bidirectional Attention Flow for Machine Comprehension, ICLR17 PDF + code PDF
Ceyer Image-to-Markup Generation with Coarse-to-Fine Attention, ICML17 PDF + code PDF
ChaoJiang Can Active Memory Replace Attention? ; Samy Bengio, NIPS16 PDF PDF
  An Information-Theoretic Framework for Fast and Robust Unsupervised Learning via Neural Population Infomax, ICLR17 PDF  

Structure V - DNN with Memory

Presenter Papers Information OurPresentation
Tianlu Ask Me Anything: Dynamic Memory Networks for Natural Language Processing, ICML17 PDF + code PDF
Jack Reasoning with Memory Augmented Neural Networks for Language Comprehension, ICLR17 PDF PDF
Xueying State-Frequency Memory Recurrent Neural Networks, ICML17 PDF PDF

Structure VI - DNN with Adaptive Structures

Presenter Papers Information OurPresentation
Anant AdaNet: Adaptive Structural Learning of Artificial Neural Networks, ICML17 PDF PDF
Shijia SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization, ICML17 PDF PDF
Jack Proximal Deep Structured Models, NIPS16 PDF PDF
  Optimal Architectures in a Solvable Model of Deep Networks, NIPS16 PDF  

Structures18- More Attention in DNN

Presenter Papers Information OurPresentation
Arshdeep Show, Attend and Tell: Neural Image Caption Generation with Visual Attention PDF PDF
Arshdeep Latent Alignment and Variational Attention PDF PDF

Application18- A few DNN for Question Answering

Presenter Papers Information OurPresentation
Derrick GloVe: Global Vectors for Word Representation PDF PDF
Derrick PARL.AI: A unified platform for sharing, training and evaluating dialog models across many tasks. URL PDF
Derrick scalable nearest neighbor algorithms for high dimensional data (PAMI14) PDF PDF
Derrick StarSpace: Embed All The Things! PDF PDF
Derrick Weaver: Deep Co-Encoding of Questions and Documents for Machine Reading, Martin Raison, Pierre-Emmanuel Mazaré, Rajarshi Das, Antoine Bordes PDF PDF

Structures18- DNN for Relations

Presenter Papers Information OurPresentation
Arshdeep Semi-Amortized Variational Autoencoders, Yoon Kim, Sam Wiseman, Andrew C. Miller, David Sontag, Alexander M. Rush PDF PDF
Arshdeep Synthesizing Programs for Images using Reinforced Adversarial Learning, Yaroslav Ganin, Tejas Kulkarni, Igor Babuschkin, S.M. Ali Eslami, Oriol Vinyals PDF PDF
Arshdeep Towards Gene Expression Convolutions using Gene Interaction Graphs, Francis Dutil, Joseph Paul Cohen, Martin Weiss, Georgy Derevyanko, Yoshua Bengio PDF PDF
Arshdeep Modularity Matters: Learning Invariant Relational Reasoning Tasks, Jason Jo, Vikas Verma, Yoshua Bengio PDF PDF


3Reliable

No. Date Title and Information PaperYear
1 2017, Feb, 22 Reliable17-Secure Machine Learning 2017-team
2 2017, Jul, 22 Reliable17-Testing and Machine Learning Basics 2017-team
3 2017, Oct, 10 Reliable Applications I - Understanding 2017-W8
4 2017, Oct, 12 Reliable Applications II - Data 2017-W8
5 2017, Oct, 17 Reliable Applications III - Data 2017-W9
6 2017, Oct, 19 Reliable Applications IV - Robustness to Data 2017-W9
7 2017, Oct, 24 Reliable Applications V - Understanding 2017-W10
8 2017, Oct, 26 Reliable Applications VI - Robustness 2017-W10
9 2018, Jan, 10 Application18- Property of DeepNN Models and More 2018-team
10 2018, Mar, 20 Reliable18- Adversarial Attacks and DNN 2018-team
11 2018, May, 20 Reliable18- Adversarial Attacks and DNN and More 2018-team
12 2018, Aug, 3 Reliable18- Testing and Verifying DNNs 2018-team
13 2018, Aug, 13 Application18- DNNs in a Few BioMedical Tasks 2018-team

Reliable17-Secure Machine Learning

Presenter Papers Information OurPresentation
Tobin Summary of A few Papers on: Machine Learning and Cryptography, (e.g., learning to Protect Communications with Adversarial Neural Cryptography) PDF PDF
Tobin Privacy Aware Learning (NIPS12) PDF PDF
Tobin Can Machine Learning be Secure?(2006) PDF PDF

Reliable17-Testing and Machine Learning Basics

Presenter Papers Information OurPresentation
Ji A few useful things to know about machine learning PDF PDF
Ji A few papers related to testing learning, e.g., Understanding Black-box Predictions via Influence Functions PDF PDF
Ji Automated White-box Testing of Deep Learning Systems PDF PDF
Ji Testing and Validating Machine Learning Classifiers by Metamorphic Testing PDF PDF
Ji Software testing: a research travelogue (2000–2014) PDF PDF

Reliable Applications I - Understanding

Presenter Papers Information OurPresentation
Rita Learning Important Features Through Propagating Activation Differences, ICML17 PDF PDF
Ji Examples are not Enough, Learn to Criticize! Model Criticism for Interpretable Machine Learning, NIPS16 PDF PDF

Reliable Applications II - Data

Presenter Papers Information OurPresentation
Xueying Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data, ICLR17 PDF PDF
Bargav Deep Learning with Differential Privacy, CCS16 PDF + video PDF
Bargav Privacy-Preserving Deep Learning, CCS15 PDF PDF

Reliable Applications III - Data

Presenter Papers Information OurPresentation
Jack Learning to Query, Reason, and Answer Questions On Ambiguous Texts, ICLR17 PDF PDF
Beilun Conditional Image Generation with Pixel CNN Decoders, NIPS16 PDF PDF

Reliable Applications IV - Robustness to Data

Presenter Papers Information OurPresentation
Ji Delving into Transferable Adversarial Examples and Black-box Attacks,ICLR17 pdf PDF
Shijia On Detecting Adversarial Perturbations, ICLR17 pdf PDF
Anant Parseval Networks: Improving Robustness to Adversarial Examples, ICML17 pdf PDF
Bargav Being Robust (in High Dimensions) Can Be Practical, ICML17 pdf PDF
  Data Noising as Smoothing in Neural Network Language Models (Ng), ICLR17 pdf  
  Measuring Sample Quality with Kernels, NIPS16 PDF  

Reliable Applications V - Understanding

Presenter Papers Information OurPresentation
ChaoJiang Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity, NIPS16 PDF PDF
Rita Visualizing Deep Neural Network Decisions: Prediction Difference Analysis, ICLR17 PDF PDF
Xueying Domain Separation Networks, NIPS16 PDF PDF
  The Robustness of Estimator Composition, NIPS16 PDF  

Reliable Applications VI - Robustness

Presenter Papers Information OurPresentation
Tianlu Robustness of classifiers: from adversarial to random noise, NIPS16 PDF PDF
Anant Blind Attacks on Machine Learners, NIPS16 PDF PDF
Arshdeep Axiomatic Attribution for Deep Networks, ICML17 PDF PDF

Application18- Property of DeepNN Models and More

Presenter Papers Information OurPresentation
Bill Measuring the tendency of CNNs to Learn Surface Statistical Regularities Jason Jo, Yoshua Bengio PDF PDF
Bill Generating Sentences by Editing Prototypes, Kelvin Guu, Tatsunori B. Hashimoto, Yonatan Oren, Percy Liang PDF PDF
Bill Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation PDF PDF

Reliable18- Adversarial Attacks and DNN

Presenter Papers Information OurPresentation
Bill Intriguing Properties of Adversarial Examples, Ekin D. Cubuk, Barret Zoph, Samuel S. Schoenholz, Quoc V. Le PDF PDF
Bill Adversarial Spheres PDF PDF
Bill Adversarial Transformation Networks: Learning to Generate Adversarial Examples, Shumeet Baluja, Ian Fischer PDF PDF
Bill Thermometer encoding: one hot way to resist adversarial examples PDF PDF

Reliable18- Adversarial Attacks and DNN and More

Presenter Papers Information OurPresentation
Bill Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples PDF PDF
Bill Adversarial Examples for Evaluating Reading Comprehension Systems, Robin Jia, Percy Liang PDF PDF
Bill Certified Defenses against Adversarial Examples, Aditi Raghunathan, Jacob Steinhardt, Percy Liang PDF PDF
Bill Provably Minimally-Distorted Adversarial Examples, Nicholas Carlini, Guy Katz, Clark Barrett, David L. Dill PDF PDF

Reliable18- Testing and Verifying DNNs

Presenter Papers Information OurPresentation
Ji Deep Reinforcement Fuzzing, Konstantin Böttinger, Patrice Godefroid, Rishabh Singh PDF PDF
Ji Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks, Guy Katz, Clark Barrett, David Dill, Kyle Julian, Mykel Kochenderfer PDF PDF
Ji DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars, Yuchi Tian, Kexin Pei, Suman Jana, Baishakhi Ray PDF PDF
Ji A few Recent (2018) papers on Black-box Adversarial Attacks, like Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors PDF PDF
Ji 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) PDF PDF
Bill On the importance of single directions for generalization, Ari S. Morcos, David G.T. Barrett, Neil C. Rabinowitz, Matthew Botvinick PDF PDF

Application18- DNNs in a Few BioMedical Tasks

Presenter Papers Information OurPresentation
Arshdeep DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. PDF PDF
Arshdeep Solving the RNA design problem with reinforcement learning, PLOSCB PDF PDF
Arshdeep The CRISPR tool kit for genome editing and beyond, Mazhar Adli PDF PDF
Arshdeep deepCRISPR: optimized CRISPR guide RNA design by deep learning PDF PDF
Arshdeep Deep learning sequence-based ab initio prediction of variant effects on ex pression and disease risk PDF PDF


4Optimization

No. Date Title and Information PaperYear
1 2017, Apr, 22 Optimization17- Optimization in DNN 2017-team
2 2017, Oct, 31 Optimization I - Understanding DNN Optimization 2017-W11
3 2017, Nov, 2 Optimization II - DNN for Optimization 2017-W11
4 2017, Nov, 7 Optimization III - Optimization for DNN 2017-W12
5 2017, Nov, 9 Optimization IV - DNN for Optimization 2 2017-W12

Optimization17- Optimization in DNN

Presenter Papers Information OurPresentation
Muthu Optimization Methods for Large-Scale Machine Learning, Léon Bottou, Frank E. Curtis, Jorge Nocedal PDF PDF
Muthu Fast Training of Recurrent Networks Based on EM Algorithm (1998) PDF PDF
Muthu FitNets: Hints for Thin Deep Nets, ICLR15 PDF PDF
Muthu Two NIPS 2015 Deep Learning Optimization Papers PDF PDF
Muthu Difference Target Propagation (2015) PDF PDF

Optimization I - Understanding DNN Optimization

Presenter Papers Information OurPresentation
Ceyer An overview of gradient optimization algorithms, PDF PDF
Shijia Osborne - Probabilistic numerics for deep learning DLSS 2017 + Video PDF / PDF2
Jack Automated Curriculum Learning for Neural Networks, ICML17 PDF PDF
  Johnson - Automatic Differentiation slide + video  

Optimization II - DNN for Optimization

Presenter Papers Information OurPresentation
Ji Neural Architecture Search with Reinforcement Learning, ICLR17 PDF PDF
Ceyer Learning to learn DLSS17video PDF
Beilun Optimization as a Model for Few-Shot Learning, ICLR17 PDF + More PDF
  Batched High-dimensional Bayesian Optimization via Structural Kernel Learning PDF  

Optimization III - Optimization for DNN

Presenter Papers Information OurPresentation
Ji Forward and Reverse Gradient-Based Hyperparameter Optimization, ICML17 PDF PDF
Chaojiang Adaptive Neural Networks for Efficient Inference, ICML17 PDF PDF
Bargav Practical Gauss-Newton Optimisation for Deep Learning, ICML17 PDF PDF
Rita How to Escape Saddle Points Efficiently, ICML17 PDF PDF
Beilun+Arshdeep Mollifying Networks, Bengio, ICLR17 PDF PDF / PDF2

Optimization IV - DNN for Optimization 2

Presenter Papers Information OurPresentation
Anant Neural Optimizer Search with Reinforcement Learning, ICML17 PDF PDF
Shijia Professor Forcing: A New Algorithm for Training Recurrent Networks, NIPS16 PDF + Video PDF
  Sharp Minima Can Generalize For Deep Nets, ICML17 PDF  


5Generative

No. Date Title and Information PaperYear
1 2017, May, 22 Generative17- Generative Deep Networks 2017-team
2 2017, Aug, 31 Generative I - GAN tutorial by Ian Goodfellow 2017-W2
3 2017, Nov, 14 Generative II - Deep Generative Models 2017-W13
4 2017, Nov, 16 Generative III - GAN and More 2017-W13
5 2018, Apr, 20 Generative18 -Generative Adversarial Network (classified) 2018-team
6 2018, Aug, 23 Generative18 -A few more DNN Generative Models 2018-team

Generative17- Generative Deep Networks

Presenter Papers Information OurPresentation
Tobin Energy-Based Generative Adversarial Network PDF PDF
Jack ThreeDeepGenerativeModels PDF PDF
Muthu Deep Compression: Compressing Deep Neural Networks (ICLR 2016) PDF PDF

Generative I - GAN tutorial by Ian Goodfellow

GAN tutorial (NIPS 2016) paper + video + code
Generative Models I - DLSS 2017 slideraw + video + slide

Generative II - Deep Generative Models

Presenter Papers Information OurPresentation
ChaoJiang Courville - Generative Models II DLSS17Slide + video PDF
Ji Attend, Infer, Repeat: Fast Scene Understanding with Generative Models, NIPS16 PDF + talk PDF
Arshdeep Composing graphical models with neural networks for structured representations and fast inference, NIPS16 PDF PDF
  Johnson - Graphical Models and Deep Learning DLSSSlide + video  
  Parallel Multiscale Autoregressive Density Estimation, ICML17 PDF  

Generative III - GAN and More

Presenter Papers Information OurPresentation
Shijia Marrying Graphical Models & Deep Learning DLSS17 + Video PDF
Arshdeep Generalization and Equilibrium in Generative Adversarial Nets (ICML17) PDF + video PDF
Arshdeep Mode Regularized Generative Adversarial Networks (ICLR17) PDF PDF
Bargav Improving Generative Adversarial Networks with Denoising Feature Matching, ICLR17 PDF PDF
Anant Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy, ICLR17 PDF + code PDF
  McGan: Mean and Covariance Feature Matching GAN, PMLR 70:2527-2535 PDF  
  Wasserstein GAN, ICML17 PDF  

Generative18 -Generative Adversarial Network (classified)

Presenter Papers Information OurPresentation
BrandonLiu Summary of Recent Generative Adversarial Networks (Classified)   PDF
Jack Generating and designing DNA with deep generative models, Nathan Killoran, Leo J. Lee, Andrew Delong, David Duvenaud, Brendan J. Frey PDF PDF
Ji More about basics of GAN   PDF

Generative18 -A few more DNN Generative Models

Presenter Papers Information OurPresentation
Arshdeep The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables, Chris J. Maddison, Andriy Mnih, Yee Whye Teh PDF PDF
Arshdeep Feedback GAN (FBGAN) for DNA: a Novel Feedback-Loop Architecture for Optimizing Protein Functions PDF PDF
Ji Summary Of Several Autoencoder models PDF PDF
Ji Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models, Jesse Engel, Matthew Hoffman, Adam Roberts PDF PDF
Ji Summary of A Few Recent Papers about Discrete Generative models, SeqGAN, MaskGAN, BEGAN, BoundaryGAN PDF PDF
  Geometrical Insights for Implicit Generative Modeling, L Bottou, M Arjovsky, D Lopez-Paz, M Oquab PDF  


6Reinforcement

No. Date Title and Information PaperYear
1 2017, Aug, 29 Reinforcement I - Pineau - RL Basic Concepts 2017-W2
2 2017, Nov, 21 RL II - Basic tutorial RLSS17 2017-W14
3 2017, Nov, 28 RL III - Basic tutorial RLSS17 (2) 2017-W14
4 2017, Nov, 30 RL IV - RL with varying structures 2017-W15
5 2018, Aug, 3 Reliable18- Testing and Verifying DNNs 2018-team
6 2018, Aug, 13 Application18- DNNs in a Few BioMedical Tasks 2018-team

Reinforcement I - Pineau - RL Basic Concepts

Pineau - RL Basic Concepts

DLSS16 video
RLSS17 slideRaw + video+ slide

RL II - Basic tutorial RLSS17

Presenter Papers Information OurPresentation
Jack Hasselt - Deep Reinforcement Learning RLSS17.pdf + video PDF
Tianlu Roux - RL in the Industry RLSS17.pdf + video PDF / PDF-Bandit
Xueying Singh - Steps Towards Continual Learning pdf + video PDF
Ji Distral: Robust Multitask Reinforcement Learning PDF PDF

RL III - Basic tutorial RLSS17 (2)

Presenter Papers Information OurPresentation
Anant The Predictron: End-to-End Learning and Planning, ICLR17 PDF PDF
ChaoJiang Szepesvari - Theory of RL RLSS.pdf + Video PDF
Ji Mastering the game of Go without human knowledge / Nature 2017 PDF PDF
  Thomas - Safe Reinforcement Learning RLSS17.pdf + video  
  Sutton - Temporal-Difference Learning RLSS17.pdf + Video  

RL IV - RL with varying structures

Presenter Papers Information OurPresentation
Ceyer Reinforcement Learning with Unsupervised Auxiliary Tasks, ICLR17 PDF PDF
Beilun Why is Posterior Sampling Better than Optimism for Reinforcement Learning? Ian Osband, Benjamin Van Roy PDF PDF
Ji Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction, ICML17 PDF PDF
Xueying End-to-End Differentiable Adversarial Imitation Learning, ICML17 PDF PDF
  Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs, ICML17 PDF  
  Cooperative Visual Dialogue with Deep RL RLSS17pdf + video  
  FeUdal Networks for Hierarchical Reinforcement Learning, ICML17 PDF  

Reliable18- Testing and Verifying DNNs

Presenter Papers Information OurPresentation
Ji Deep Reinforcement Fuzzing, Konstantin Böttinger, Patrice Godefroid, Rishabh Singh PDF PDF
Ji Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks, Guy Katz, Clark Barrett, David Dill, Kyle Julian, Mykel Kochenderfer PDF PDF
Ji DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars, Yuchi Tian, Kexin Pei, Suman Jana, Baishakhi Ray PDF PDF
Ji A few Recent (2018) papers on Black-box Adversarial Attacks, like Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors PDF PDF
Ji 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) PDF PDF
Bill On the importance of single directions for generalization, Ari S. Morcos, David G.T. Barrett, Neil C. Rabinowitz, Matthew Botvinick PDF PDF

Application18- DNNs in a Few BioMedical Tasks

Presenter Papers Information OurPresentation
Arshdeep DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. PDF PDF
Arshdeep Solving the RNA design problem with reinforcement learning, PLOSCB PDF PDF
Arshdeep The CRISPR tool kit for genome editing and beyond, Mazhar Adli PDF PDF
Arshdeep deepCRISPR: optimized CRISPR guide RNA design by deep learning PDF PDF
Arshdeep Deep learning sequence-based ab initio prediction of variant effects on ex pression and disease risk PDF PDF


7BioApplications

No. Date Title and Information PaperYear
1 2018, Aug, 13 Application18- DNNs in a Few BioMedical Tasks 2018-team

Application18- DNNs in a Few BioMedical Tasks

Presenter Papers Information OurPresentation
Arshdeep DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. PDF PDF
Arshdeep Solving the RNA design problem with reinforcement learning, PLOSCB PDF PDF
Arshdeep The CRISPR tool kit for genome editing and beyond, Mazhar Adli PDF PDF
Arshdeep deepCRISPR: optimized CRISPR guide RNA design by deep learning PDF PDF
Arshdeep Deep learning sequence-based ab initio prediction of variant effects on ex pression and disease risk PDF PDF


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