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Related: my flashcards repo.

• The Case for Learned Index Structures [notes] [link]

• Bayesian Methods for Hackers, Chapter 2 [notes] [link]
• Bayesian Methods for Hackers, Chapter 1 [notes] [link]
• Stability and Generalization [notes] [link]

• Overheard at NIPS '17 [notes]
• Learning disentangled representations for RL [notes]
• Inductive Representation Learning on Large Graphs [notes]
• Style Transfer from Non-parallel Text by Cross-Alignment [notes]
• Counterfactual fairness [notes]
• Probabilistic methods spotlights [notes]
• Algorithms spotlights [notes]
• Interpretibility spotlights [notes]
• Architectures spotlights [notes]
• Learning State Representations [notes]
• Overcoming Limited Data with GANs [notes]
• What’s so Hard About Natural Language Understanding? [notes]
• Probabilistic Programming with Pyro [notes]
• Exploring the different paths to achievingtalks/ disentangled representations [notes]
• Priors to help automatically discover and disentangle explanatory factors [notes]
• Interpretable Discovery in Large Image Data Sets [notes]
• Generative Adversarial Imitation Learning [notes]
• Translating a Trillion Points of Data into Diagnostics, Therapies and New Insights in Health & Disease [notes]
• Causal inference, ML for the developing world [notes]
• Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning [notes]
• Medical Machine Intelligence [notes]
• ML for Health Keynote (Fei-Fei Li): Illuminating the Dark Spaces of Healthcare [notes]
• Are GANs creative? [notes]
• Computational Methods for Solving Developing World Problems [notes]
• On Bayesian Deep Learning and Deep Bayesian Learning [notes]

• Fairness in ML [notes]
• A Primer on Optimal Transport [link] [notes]
• Quick summaries: other generalization papers [notes]
• Rethinking generalization requires revisiting old ideas [link] [notes]
• Generalization in Deep Learning [link] [notes]
• Understanding deep learning requires rethinking generalization [link] [notes]
• Generative Adversarial Nets: An Overview [link] [notes]
• Concrete Problems in AI Safety [link] [notes]
• Dynamic Routing Between Capsules [link] [notes]

• Quick summaries: Wasserstein GAN, BEGAN, Batch and Weight Normalization in GANs [notes]
• Opportunities And Obstacles For Deep Learning In Biology And Medicine [link] [notes]
• Single-Channel Multi-Speaker Separation using Deep Clustering [link] [notes]
• Deep clustering: Discriminative embeddings for segmentation and separation [link] [notes]
• Learning Hierarchical Features for Scene Labeling [link] [notes]
• Pixel Recurrent Neural Networks [link] [notes]
• Artistic Style Transfer For Videos [link] [notes]
• Grammar as a Foreign Language [link] [notes]
• Deep Convolutional Neural Network Design Patterns [link] [notes]
• Massive Exploration of Neural Machine Translation Architectures [link] [notes]
• Attention Is All You Need [link] [notes]

• Quick summaries: attention papers [notes]
• DeViSE: A Deep Visual-Semantic Embedding Model [link] [notes]
• DRAW: Deep Recurrent Attentive Writer [link] [blog] md

• A Biomedical Information Extraction Primer for NLP Researchers [link] [notes]
• FaceNet: A Unified Embedding for Face Recognition and Clustering [link]
• Neural Machine Translation by Jointly Learning to Align and Translate [link] [blog]
• Enriching Word Vectors with Subword Information [link]
• Bag of Tricks for Efficient Text Classification [link] [blog]
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling [link] [blog]
• GloVe: Global Vectors for Word Representations [link]
• Recurrent Convolutional Neural Networks for Text Classification [link] [blog]

• Hierarchical Multiscale Recurrent Neural Networks [link] [blog]
• Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation [link]
• Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding [link] [blog]

• Resnet in Resnet: Generalizing Residual Architectures [link]
• Residual Networks of Residual Networks: Multilevel Residual Networks [link] [notes]
• Deep Residual Learning for Image Recognition [link] [notes]

• Adrian Colyer [link]
• Denny Britz [link]
• Hoang Cuong [link]
• Alexander Jung [link]