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

- 20 Feb 2018Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Dr. Qi | A quick and rough survey of Deep-Neural-Networks | ||

Click on a tag to see relevant list of readings.

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 |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Dr. Qi | A quick and rough survey of Deep-Neural-Networks | ||

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Dr. Qi | Making Deep Learning Understandable for Analyzing Sequential Data about Gene Regulation | ||

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 |

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 |

DLSS16 | video |

DLSS17 | video + slide |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Rita | On the Expressive Power of Deep Neural Networks | ||

Arshdeep | Understanding deep learning requires rethinking generalization, ICLR17 | ||

Tianlu | On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima, ICLR17 |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Rita | Learning Kernels with Random Features, Aman Sinha*; John Duchi, | ||

Beilun | Learning Deep Parsimonious Representations, NIPS16 | ||

Jack | Dense Associative Memory for Pattern Recognition, NIPS16 | PDF + video | |

On the Expressive Efficiency of Overlapping Architectures of Deep Learning | DLSSpdf + video |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Tianlu | Large-Scale Evolution of Image Classifiers, ICML17 | ||

Ceyer | A Closer Look at Memorization in Deep Networks, ICML17 | ||

Bargav | Learning Structured Sparsity in Deep Neural Networks, NIPS16 | ||

Arshdeep | Learning the Number of Neurons in Deep Networks, NIPS16 |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

SE | Equivariance Through Parameter-Sharing, ICML17 | ||

SE | Why Deep Neural Networks for Function Approximation?, ICLR17 | ||

SE | Geometry of Neural Network Loss Surfaces via Random Matrix Theory, ICML17 | ||

SE | Deep learning in the brain | DLSS17 + Video |

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 |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Muthu | Matching Networks for One Shot Learning (NIPS16) | ||

Jack | Meta-Learning with Memory-Augmented Neural Networks (ICML16) |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Arshdeep | HyperNetworks, David Ha, Andrew Dai, Quoc V. Le ICLR 2017 | ||

Arshdeep | Learning feed-forward one-shot learners | ||

Arshdeep | Learning to Learn by gradient descent by gradient descent | ||

Arshdeep | https://arxiv.org/abs/1605.09673 |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Muthu | NIPS Embedding Papers survey 2012 to 2015 | NIPS | |

Tobin | Binary embeddings with structured hashed projections |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Arshdeep | Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction | ||

Arshdeep | Decoupled Neural Interfaces Using Synthetic Gradients | ||

Arshdeep | Diet Networks: Thin Parameters for Fat Genomics | ||

Arshdeep | Metric Learning with Adaptive Density Discrimination |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Jack | Learning End-to-End Goal-Oriented Dialog, ICLR17 | ||

Arshdeep | Making Neural Programming Architectures Generalize via Recursion, ICLR17 | ||

Bargav | Nonparametric Neural Networks, ICLR17 |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Shijia | Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer, (Dean), ICLR17 | ||

Xueying | Towards Deep Interpretability (MUS-ROVER II): Learning Hierarchical Representations of Tonal Music, ICLR17 | ||

Ceyer | Sequence Modeling via Segmentations, ICML17 | ||

Arshdeep | Input Switched Affine Networks: An RNN Architecture Designed for Interpretability, ICML17 |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Rita | Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer, ICLR17 | ||

Tianlu | Dynamic Coattention Networks For Question Answering, ICLR17 | PDF + code | |

ChaoJiang | Structured Attention Networks, ICLR17 | PDF + code |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Jack | Attend, Adapt and Transfer: Attentive Deep Architecture for Adaptive Transfer from multiple sources in the same domain, ICLR17 | ||

Arshdeep | Bidirectional Attention Flow for Machine Comprehension, ICLR17 | PDF + code | |

Ceyer | Image-to-Markup Generation with Coarse-to-Fine Attention, ICML17 | PDF + code | |

ChaoJiang | Can Active Memory Replace Attention? ; Samy Bengio, NIPS16 | ||

An Information-Theoretic Framework for Fast and Robust Unsupervised Learning via Neural Population Infomax, ICLR17 |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Tianlu | Ask Me Anything: Dynamic Memory Networks for Natural Language Processing, ICML17 | PDF + code | |

Jack | Reasoning with Memory Augmented Neural Networks for Language Comprehension, ICLR17 | ||

Xueying | State-Frequency Memory Recurrent Neural Networks, ICML17 |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Anant | AdaNet: Adaptive Structural Learning of Artificial Neural Networks, ICML17 | ||

Shijia | SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization, ICML17 | ||

Jack | Proximal Deep Structured Models, NIPS16 | ||

Optimal Architectures in a Solvable Model of Deep Networks, NIPS16 |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Arshdeep | Show, Attend and Tell: Neural Image Caption Generation with Visual Attention | ||

Arshdeep | Latent Alignment and Variational Attention |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

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) | ||

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 |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Arshdeep | Semi-Amortized Variational Autoencoders, Yoon Kim, Sam Wiseman, Andrew C. Miller, David Sontag, Alexander M. Rush | ||

Arshdeep | Synthesizing Programs for Images using Reinforced Adversarial Learning, Yaroslav Ganin, Tejas Kulkarni, Igor Babuschkin, S.M. Ali Eslami, Oriol Vinyals | ||

Arshdeep | Towards Gene Expression Convolutions using Gene Interaction Graphs, Francis Dutil, Joseph Paul Cohen, Martin Weiss, Georgy Derevyanko, Yoshua Bengio | ||

Arshdeep | Modularity Matters: Learning Invariant Relational Reasoning Tasks, Jason Jo, Vikas Verma, Yoshua Bengio |

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 |

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) | ||

Tobin | Privacy Aware Learning (NIPS12) | ||

Tobin | Can Machine Learning be Secure?(2006) |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Ji | A few useful things to know about machine learning | ||

Ji | A few papers related to testing learning, e.g., Understanding Black-box Predictions via Influence Functions | ||

Ji | Automated White-box Testing of Deep Learning Systems | ||

Ji | Testing and Validating Machine Learning Classifiers by Metamorphic Testing | ||

Ji | Software testing: a research travelogue (2000–2014) |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Rita | Learning Important Features Through Propagating Activation Differences, ICML17 | ||

Ji | Examples are not Enough, Learn to Criticize! Model Criticism for Interpretable Machine Learning, NIPS16 |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Xueying | Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data, ICLR17 | ||

Bargav | Deep Learning with Differential Privacy, CCS16 | PDF + video | |

Bargav | Privacy-Preserving Deep Learning, CCS15 |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Jack | Learning to Query, Reason, and Answer Questions On Ambiguous Texts, ICLR17 | ||

Beilun | Conditional Image Generation with Pixel CNN Decoders, NIPS16 |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Ji | Delving into Transferable Adversarial Examples and Black-box Attacks,ICLR17 | ||

Shijia | On Detecting Adversarial Perturbations, ICLR17 | ||

Anant | Parseval Networks: Improving Robustness to Adversarial Examples, ICML17 | ||

Bargav | Being Robust (in High Dimensions) Can Be Practical, ICML17 | ||

Data Noising as Smoothing in Neural Network Language Models (Ng), ICLR17 | |||

Measuring Sample Quality with Kernels, NIPS16 |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

ChaoJiang | Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity, NIPS16 | ||

Rita | Visualizing Deep Neural Network Decisions: Prediction Difference Analysis, ICLR17 | ||

Xueying | Domain Separation Networks, NIPS16 | ||

The Robustness of Estimator Composition, NIPS16 |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Tianlu | Robustness of classifiers: from adversarial to random noise, NIPS16 | ||

Anant | Blind Attacks on Machine Learners, NIPS16 | ||

Arshdeep | Axiomatic Attribution for Deep Networks, ICML17 |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

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 | ||

Bill | Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Bill | Intriguing Properties of Adversarial Examples, Ekin D. Cubuk, Barret Zoph, Samuel S. Schoenholz, Quoc V. Le | ||

Bill | Adversarial Spheres | ||

Bill | Adversarial Transformation Networks: Learning to Generate Adversarial Examples, Shumeet Baluja, Ian Fischer | ||

Bill | Thermometer encoding: one hot way to resist adversarial examples |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

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 |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Ji | Deep Reinforcement Fuzzing, Konstantin Böttinger, Patrice Godefroid, Rishabh Singh | ||

Ji | Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks, Guy Katz, Clark Barrett, David Dill, Kyle Julian, Mykel Kochenderfer | ||

Ji | DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars, Yuchi Tian, Kexin Pei, Suman Jana, Baishakhi Ray | ||

Ji | A few Recent (2018) papers on Black-box Adversarial Attacks, like Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors | ||

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) | ||

Bill | On the importance of single directions for generalization, Ari S. Morcos, David G.T. Barrett, Neil C. Rabinowitz, Matthew Botvinick |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

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 | ||

Arshdeep | The CRISPR tool kit for genome editing and beyond, Mazhar Adli | ||

Arshdeep | deepCRISPR: optimized CRISPR guide RNA design by deep learning | ||

Arshdeep | Deep learning sequence-based ab initio prediction of variant effects on ex pression and disease risk |

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 |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Muthu | Optimization Methods for Large-Scale Machine Learning, Léon Bottou, Frank E. Curtis, Jorge Nocedal | ||

Muthu | Fast Training of Recurrent Networks Based on EM Algorithm (1998) | ||

Muthu | FitNets: Hints for Thin Deep Nets, ICLR15 | ||

Muthu | Two NIPS 2015 Deep Learning Optimization Papers | ||

Muthu | Difference Target Propagation (2015) |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Ceyer | An overview of gradient optimization algorithms, | ||

Shijia | Osborne - Probabilistic numerics for deep learning | DLSS 2017 + Video | PDF / PDF2 |

Jack | Automated Curriculum Learning for Neural Networks, ICML17 | ||

Johnson - Automatic Differentiation | slide + video |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Ji | Neural Architecture Search with Reinforcement Learning, ICLR17 | ||

Ceyer | Learning to learn | DLSS17video | |

Beilun | Optimization as a Model for Few-Shot Learning, ICLR17 | PDF + More | |

Batched High-dimensional Bayesian Optimization via Structural Kernel Learning |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Ji | Forward and Reverse Gradient-Based Hyperparameter Optimization, ICML17 | ||

Chaojiang | Adaptive Neural Networks for Efficient Inference, ICML17 | ||

Bargav | Practical Gauss-Newton Optimisation for Deep Learning, ICML17 | ||

Rita | How to Escape Saddle Points Efficiently, ICML17 | ||

Beilun+Arshdeep | Mollifying Networks, Bengio, ICLR17 | PDF / PDF2 |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Anant | Neural Optimizer Search with Reinforcement Learning, ICML17 | ||

Shijia | Professor Forcing: A New Algorithm for Training Recurrent Networks, NIPS16 | PDF + Video | |

Sharp Minima Can Generalize For Deep Nets, ICML17 |

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 |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Tobin | Energy-Based Generative Adversarial Network | ||

Jack | ThreeDeepGenerativeModels | ||

Muthu | Deep Compression: Compressing Deep Neural Networks (ICLR 2016) |

GAN tutorial (NIPS 2016) | paper + video + code |

Generative Models I - DLSS 2017 | slideraw + video + slide |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

ChaoJiang | Courville - Generative Models II | DLSS17Slide + video | |

Ji | Attend, Infer, Repeat: Fast Scene Understanding with Generative Models, NIPS16 | PDF + talk | |

Arshdeep | Composing graphical models with neural networks for structured representations and fast inference, NIPS16 | ||

Johnson - Graphical Models and Deep Learning | DLSSSlide + video | ||

Parallel Multiscale Autoregressive Density Estimation, ICML17 |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Shijia | Marrying Graphical Models & Deep Learning | DLSS17 + Video | |

Arshdeep | Generalization and Equilibrium in Generative Adversarial Nets (ICML17) | PDF + video | |

Arshdeep | Mode Regularized Generative Adversarial Networks (ICLR17) | ||

Bargav | Improving Generative Adversarial Networks with Denoising Feature Matching, ICLR17 | ||

Anant | Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy, ICLR17 | PDF + code | |

McGan: Mean and Covariance Feature Matching GAN, PMLR 70:2527-2535 | |||

Wasserstein GAN, ICML17 |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

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 | ||

Ji | More about basics of GAN |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Arshdeep | The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables, Chris J. Maddison, Andriy Mnih, Yee Whye Teh | ||

Arshdeep | Feedback GAN (FBGAN) for DNA: a Novel Feedback-Loop Architecture for Optimizing Protein Functions | ||

Ji | Summary Of Several Autoencoder models | ||

Ji | Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models, Jesse Engel, Matthew Hoffman, Adam Roberts | ||

Ji | Summary of A Few Recent Papers about Discrete Generative models, SeqGAN, MaskGAN, BEGAN, BoundaryGAN | ||

Geometrical Insights for Implicit Generative Modeling, L Bottou, M Arjovsky, D Lopez-Paz, M Oquab |

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 |

DLSS16 | video |

RLSS17 | slideRaw + video+ slide |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

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 | |

Ji | Distral: Robust Multitask Reinforcement Learning |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Anant | The Predictron: End-to-End Learning and Planning, ICLR17 | ||

ChaoJiang | Szepesvari - Theory of RL | RLSS.pdf + Video | |

Ji | Mastering the game of Go without human knowledge / Nature 2017 | ||

Thomas - Safe Reinforcement Learning | RLSS17.pdf + video | ||

Sutton - Temporal-Difference Learning | RLSS17.pdf + Video |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Ceyer | Reinforcement Learning with Unsupervised Auxiliary Tasks, ICLR17 | ||

Beilun | Why is Posterior Sampling Better than Optimism for Reinforcement Learning? Ian Osband, Benjamin Van Roy | ||

Ji | Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction, ICML17 | ||

Xueying | End-to-End Differentiable Adversarial Imitation Learning, ICML17 | ||

Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs, ICML17 | |||

Cooperative Visual Dialogue with Deep RL | RLSS17pdf + video | ||

FeUdal Networks for Hierarchical Reinforcement Learning, ICML17 |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

Ji | Deep Reinforcement Fuzzing, Konstantin Böttinger, Patrice Godefroid, Rishabh Singh | ||

Ji | Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks, Guy Katz, Clark Barrett, David Dill, Kyle Julian, Mykel Kochenderfer | ||

Ji | DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars, Yuchi Tian, Kexin Pei, Suman Jana, Baishakhi Ray | ||

Ji | A few Recent (2018) papers on Black-box Adversarial Attacks, like Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors | ||

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) | ||

Bill | On the importance of single directions for generalization, Ari S. Morcos, David G.T. Barrett, Neil C. Rabinowitz, Matthew Botvinick |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

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 | ||

Arshdeep | The CRISPR tool kit for genome editing and beyond, Mazhar Adli | ||

Arshdeep | deepCRISPR: optimized CRISPR guide RNA design by deep learning | ||

Arshdeep | Deep learning sequence-based ab initio prediction of variant effects on ex pression and disease risk |

No. | Date | Title and Information | PaperYear |
---|---|---|---|

1 | 2018, Aug, 13 | Application18- DNNs in a Few BioMedical Tasks | 2018-team |

Presenter | Papers | Information | OurPresentation |
---|---|---|---|

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 | ||

Arshdeep | The CRISPR tool kit for genome editing and beyond, Mazhar Adli | ||

Arshdeep | deepCRISPR: optimized CRISPR guide RNA design by deep learning | ||

Arshdeep | Deep learning sequence-based ab initio prediction of variant effects on ex pression and disease risk |