Notes in Markdown about papers I read, mainly general Machine Learning and Deep Learning papers.
- A Tough to Beat Baseline for Sentence Embeddings (2017)
- Adversarial Feature Learning (2017)
- An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling (2018)
- Are All GANs Created Equal ? (2017)
- Attention is all you Need (2017)
- AutoAugment : Learning Augmentation Policies from Data (2018)
- Autoregressive ConvNets for Asynchronous Time Series (2017)
- Bag of Tricks for Efficient Text Classification (2016)
- Combining Hyperband and Bayesian Optimization (2017)
- Distilling the Knowledge of a Neural Network (2015)
- Deep Reinforcement Learning that Matters (2017)
- Explaining the Predictions of Any Classifier (2016)
- Generalization in Deep Learning (2017)
- Glow : Generative Flow with Invertible 1x1 Convolutions (2018)
- HOGWILD! : a Lock-Free Approach to Parallelizing SGD (2011)
- Inferring and Executing Programs for Visual Reasoning (2017)
- Learning through Dialogue Interactions by Asking Questions (2017)
- Letter-Based Speech Recognition with Gated ConvNets (2017)
- Natural Language Processing with Small Feed-Forward Networks (2017)
- Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders (2017)
- On the Convergence of Adam and Beyond (2018)
- Playing Atari with 6 Neurons (2018)
- Playing Hard Exploration Games by Watching YouTube (2018)
- Reading Wikipedia to Answer Open-Domain Questions (2017)
- Realistic Evaluation of Semi-Supervised Algorithms (2018)
- Self-Normalizing Networks (2017)
- Semi-Supervised Learning with Ladder Networks (2015)
- Snapshot Ensembles: train 1, get M for free (2017)
- Temporal Ensembling for Semi-Supervised Learning (2016)
- Understanding Black-box Predictions via Influence Functions (2017)
- Understanding Deep Learning Requires Rethinking Generalization (2016)
- Unsupervised Representation Learning by Predicting Image Rotations (2018)
Occasionally I like to present interesting papers and methods :