Collection of machine learning resources
Improving neural networks by preventing co-adaptation of feature detectors
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Generative Adversarial Networks
Adversarial Generation of Natural Language
Twin Networks: Using the Future as a Regularizer
Learning to Transduce with Unbounded Memory
Sequence to Sequence Learning with Neural Networks
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Building High-level Features Using Large Scale Unsupervised Learning
Learning Fine-grained Image Similarity with Deep Ranking
Mastering the game of Go with deep neural networks and tree search
StarCraft II: A New Challenge for Reinforcement Learning
Distributed Representations of Sentences and Documents
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
Clustering: A neural network approach
No Free Lunch Theorems for Optimization
Rectified Linear Units Improve Restricted Boltzmann Machines
On the number of response regions of deep feedforward networks with piecewise linear activations
Theano: A CPU and GPU Math Compiler in Python
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
TensorFlow: A system for large-scale machine learning
A Critical Review of Recurrent Neural Networks for Sequence Learning
Distributed Representations of Words and Phrases and their Compositionality
Concrete Problems in AI Safety
Deep Residual Learning for Image Recognition
Improved Techniques for Training GANs
Visualizing and Understanding Convolutional Networks
ADAM: A METHOD FOR STOCHASTIC OPTIMIZATION
ADADELTA: AN ADAPTIVE LEARNING RATE METHOD
Practical Bayesian Optimization of Machine Learning Algorithms
Random Search for Hyper-Parameter Optimization
A Theoretical Framework for Back-Propagation
Gradient-Based Learning Applied to Document Recognition
On the importance of initialization and momentum in deep learning
Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition
Practical Recommendations for Gradient-Based Training of Deep Architectures
Understanding the difficulty of training deep feedforward neural networks
Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
Evolution Strategies as a Scalable Alternative to Reinforcement Learning
Stanford CS229: Machine Learning
Stanford CS231n: Convolutional Neural Networks for Visual Recognition
Stanford CS224d: Deep Learning for Natural Language Processing
Berkeley CS 294: Deep Reinforcement Learning
Berkeley CS 188: Introduction to AI
UCL Reinforcement Learning by David Silver (textbook)
Neural Networks and Deep Learning
ML, MAP, and Bayesian — The Holy Trinity of Parameter Estimation and Data Prediction
Neural Networks, Manifolds, and Topology
A Tutorial on Bayesian Belief Networks
An Introduction to Conditional Random Fields for Relational Learning
Introduction to Monte Carlo Tree Search
Learning Deep Architectures for AI
Stanford Deep Learning Tutorial
Speech Recognition with Neural Networks
Multiple different natural language processing tasks in a single deep model
Tutorial on Sheaves in Data Analytics
Visualizing TensorFlow Graphs in Jupyter Notebooks
How to get into the top 15 of a Kaggle competition using Python
Deep Learning - Convolutional Neural Networks - Architectural Zoo
Neural Machine Translation (seq2seq) Tutorial
Google Research: Deep Learning
Google Research: Neural Networks
Ilya Sutskever's Google Scholar
Pieter Abbeel's Google Scholar
Machine Intelligence Research Institute
Philippe Desjardins-Proulx blog