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deep-learning-paper-notes

★ 108 Open GitHub ↗

Summaries, notes, questions on Deep Learning research papers.

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## Paper notes
_Related: my [flashcards](https://github.com/JasonBenn/flashcards) repo._

#### 2018-03

* The Case for Learned Index Structures [[notes](papers/learned-index-structures.md)] [[link](https://arxiv.org/abs/1712.01208)]

#### 2018-02

* Bayesian Methods for Hackers, Chapter 2 [[notes](papers/bayesian-methods-2.md)] [[link](http://nbviewer.jupyter.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter2_MorePyMC/Ch2_MorePyMC_PyMC3.ipynb)]
* Bayesian Methods for Hackers, Chapter 1 [[notes](papers/bayesian-methods-1.md)] [[link](http://nbviewer.jupyter.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter1_Introduction/Ch1_Introduction_PyMC3.ipynb)]
* Stability and Generalization [[notes](papers/stability-and-generalization.md)] [[link](http://www.jmlr.org/papers/volume2/bousquet02a/bousquet02a.pdf)]

#### 2017-12

* Overheard at NIPS '17 [[notes](talks/overheard-at-nips.md)]
* Learning disentangled representations for RL [[notes](papers/learning-disentangled-representations-for-rl.md)]
* Inductive Representation Learning on Large Graphs [[notes](papers/inductive-representation-learning-on-large-graphs.md)]
* Style Transfer from Non-parallel Text by Cross-Alignment [[notes](papers/style-transfer-from-non-parallel-text-by-cross-alignment.md)]
* Counterfactual fairness [[notes](papers/counterfactual-fairness.md)]
* Probabilistic methods spotlights [[notes](talks/nips-17-spotlights-algorithms.md)]
* Algorithms spotlights [[notes](talks/nips-17-spotlights-algorithms.md)]
* Interpretibility spotlights [[notes](talks/nips-17-spotlights-interpretibility.md)]
* Architectures spotlights [[notes](talks/nips-17-spotlights-architectures.md)]
* Learning State Representations [[notes](talks/learning-state-representations.md)]
* Overcoming Limited Data with GANs [[notes](talks/overcoming-limited-data-with-gans.md)]
* What’s so Hard About Natural Language Understanding? [[notes](talks/whats-so-hard-about-natural-language-understanding.md)]
* Probabilistic Programming with Pyro [[notes](talks/probabilistic-programming-with-pyro.md)]
* Exploring the different paths to achievingtalks/ disentangled representations
[[notes](papers/exploring-the-different-paths-to-achieving-disentangled-representations.md)]
* Priors to help automatically discover and disentangle explanatory factors [[notes](talks/priors-to-help-automatically-discover-and-disentangle-explanatory-factors.md)]
* Interpretable Discovery in Large Image Data Sets [[notes](papers/interpretable-discovery-in-large-image-data-sets.md)]
* Generative Adversarial Imitation Learning [[notes](talks/generative-adversarial-imitation-learning.md)]
* Translating a Trillion Points of Data into Diagnostics, Therapies and New Insights in Health & Disease [[notes](talks/translating-a-trillion-points-of-data.md)]
* Causal inference, ML for the developing world [[notes](talks/panels-causal-inference-ml4dw.md)]
* Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning [[notes](papers/meta-learners-for-estimating-heterogeneous-treatment-effects-using-machine-learning.md)]
* Medical Machine Intelligence [[notes](talks/medical-machine-intelligence.md)]
* ML for Health Keynote (Fei-Fei Li): Illuminating the Dark Spaces of Healthcare [[notes](talks/illuminating-the-dark-spaces-of-healthcare.md)]
* Are GANs creative? [[notes](talks/are-gans-creative.md)]
* Computational Methods for Solving Developing World Problems [[notes](talks/computational-methods-for-solving-developing-world-problems.md)]
* On Bayesian Deep Learning and Deep Bayesian Learning [[notes](talks/on-bayesian-deep-learning-deep-bayesian-learning.md)]

#### 2017-11

* Fairness in ML [[notes](talks/fairness-in-ml.md)]
* A Primer on Optimal Transport [[link](https://optimaltransport.github.io/)] [[notes](talks/primer-optimal-transport.md)]
* Quick summaries: other generalization papers [[notes](papers/generalization-summaries.md)]
* Rethinking generalization requires revisiting old ideas [[link](https://arxiv.org/pdf/1710.09553.pdf)] [[notes](papers/rethinking-generalization.md)]
* Generalization in Deep Learning [[link](https://arxiv.org/abs/1710.05468)] [[notes](papers/generalization-in-deep-learning.md)]
* Understanding deep learning requires rethinking generalization [[link](https://arxiv.org/abs/1611.03530)] [[notes](papers/rethinking-generalixation.md)]
* Generative Adversarial Nets: An Overview [[link](https://arxiv.org/abs/1710.07035)] [[notes](papers/gans-an-overview.md)]
* Concrete Problems in AI Safety [[link](http://arxiv.org/abs/1606.06565)] [[notes](papers/concrete-problems-in-ai-safety.md)]
* Dynamic Routing Between Capsules [[link](https://research.google.com/pubs/pub46351.html)] [[notes](papers/dynamic-routing-between-capsules.md)]

#### 2017-10

* Quick summaries: Wasserstein GAN, BEGAN, Batch and Weight Normalization in GANs [[notes](papers/wgan-bgan-weight-norm-gans.md)]
* Opportunities And Obstacles For Deep Learning In Biology And Medicine [[link](https://www.biorxiv.org/content/early/2017/05/28/142760)] [[notes](papers/opportunities-and-obstacles-for-deep-learning-in-biology-and-medicine.md)]
* Single-Channel Multi-Speaker Separation using Deep Clustering [[link]](http://arxiv.org/abs/1607.02173) [[notes](papers/single-channel-multi-speaker-separation-using-deep-clustering.md)]
* Deep clustering: Discriminative embeddings for segmentation and separation [[link]](http://arxiv.org/abs/1508.04306) [[notes](papers/deep-clustering-discriminative-embeddings-for-segmentation-and-separation.md)]
* Learning Hierarchical Features for Scene Labeling [[link](http://yann.lecun.com/exdb/publis/pdf/farabet-pami-13.pdf)] [[notes](papers/learning-hierarchical-features-for-scene-labeling.md)]
* Pixel Recurrent Neural Networks [[link](http://arxiv.org/abs/1601.06759)] [[notes](papers/pixel-rnns.md)]
* Artistic Style Transfer For Videos [[link](http://arxiv.org/abs/1412.7449)] [[notes](papers/artistic-style-transfer-for-videos.md)]
* Grammar as a Foreign Language [[link](http://arxiv.org/abs/1412.7449)] [[notes](papers/grammar-as-a-foreign-language.md)]
* Deep Convolutional Neural Network Design Patterns [[link](http://arxiv.org/abs/1611.00847)] [[notes](papers/deep-conv-net-design-patterns.md)]
* Massive Exploration of Neural Machine Translation Architectures [[link](http://arxiv.org/abs/1703.03906)] [[notes](papers/massive-exploration-of-nmt-architectures.md)]
* Attention Is All You Need [[link](https://arxiv.org/abs/1706.03762)] [[notes](papers/attention-is-all-you-need.md)]

#### 2017-09

* Quick summaries: attention papers [[notes](papers/summaries-attention.md)]
* DeViSE: A Deep Visual-Semantic Embedding Model [[link](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/41869.pdf)] [[notes](papers/devise.md)]
* DRAW: Deep Recurrent Attentive Writer [[link](https://arxiv.org/abs/1502.04623)] [[blog](https://medium.com/paper-club/draw-generating-small-images-by-adding-attention-to-variational-autoencoders-430ba241972b)]
md
#### 2017-08

* A Biomedical Information Extraction Primer for NLP Researchers [[link](https://arxiv.org/abs/1705.05437)] [[notes](papers/biomedical-info-extraction-primer.md)]
* FaceNet: A Unified Embedding for Face Recognition and Clustering [[link](https://arxiv.org/abs/1503.03832)]
* Neural Machine Translation by Jointly Learning to Align and Translate [[link](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwi5w_-uvc3WAhVS3WMKHcMIA2oQFggrMAA&url=https%3A%2F%2Farxiv.org%2Fabs%2F1409.0473&usg=AOvVaw18karG5qoaCtgEx3nhhnT7)] [[blog](https://medium.com/paper-club/remarques-sur-la-traduction-de-la-machine-neurale-en-apprenant-ensemble-%C3%A0-aligner-et-%C3%A0-traduire-cd23004cf207)]
* Enriching Word Vectors with Subword Information [[link](https://arxiv.org/pdf/1607.04606.pdf)]
* Bag of Tricks for Efficient Text Classification [[link](https://arxiv.org/abs/1607.01759)] [[blog](https://medium.com/paper-club/fasttext-bc181f50a452)]
* Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling [[link](https://arxiv.org/abs/1412.3555)] [[blog](https://medium.com/paper-club/grus-vs-lstms-e9d8e2484848)]
* GloVe: Global Vectors for Word Representations [[link](https://nlp.stanford.edu/pubs/glove.pdf)]
* Recurrent Convolutional Neural Networks for Text Classification [[link](https://scholar.google.com/scholar?q=Recurrent+Convolutional+Neural+Networks+for+Text+Classification&btnG=&hl=en&as_sdt=0%2C5)] [[blog](https://medium.com/paper-club/cnns-for-text-classification-b45bde0bb254)]

#### 2017-07

* Hierarchical Multiscale Recurrent Neural Networks [[link](https://arxiv.org/abs/1609.01704v7)] [[blog](https://medium.com/paper-club/hierarchical-multiscale-recurrent-neural-networks-9e614e4fb04)]
* Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation [[link](https://arxiv.org/abs/1308.3432)]
* Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding [[link](https://arxiv.org/abs/1606.01847)] [[blog](https://medium.com/paper-club/multimodal-compact-bilinear-pooling-for-visual-question-answering-and-visual-grounding-6f71bc7d0566)]

#### 2017-06

* Resnet in Resnet: Generalizing Residual Architectures [[link](https://arxiv.org/abs/1603.08029)]
* Residual Networks of Residual Networks: Multilevel Residual Networks [[link](https://arxiv.org/abs/1608.02908v2)] [[notes](papers/residual-networks-of-residual-networks.md)]
* Deep Residual Learning for Image Recognition [[link](https://arxiv.org/pdf/1512.03385.pdf)] [[notes](papers/deep-residual-learning-for-image-recognition.md)]

---

#### Other collections of paper notes

* Adrian Colyer [[link](https://blog.acolyer.org/tag/deep-learning)]
* Denny Britz [[link](https://github.com/dennybritz/deeplearning-papernotes)]
* Hoang Cuong [[link](https://github.com/hoangcuong2011/Good-Papers)]
* Alexander Jung [[link](https://github.com/aleju/papers)]

deep-learning-paper-notes

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