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Paper and authors

Learning to communicate with deep multi-agent reinforcement learning (by Jakob Foerster et al.)

Learning to learn by gradient descent by gradient descent (by Marcin Andrychowicz et al.)

Learning to poke by poking: experimental learning of intuitive physics (Pulkiy AGRAWAL et al.)

Value iteration network, Aviv Tamar et al.

Cooperative Inverse Reinforcement Learning (by Dylan Hadfield-Menell et al.)

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (by Sergey Ioffe, Christian Szegedy)

Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks (by Tim Salimans Diederik P. Kingma)

Introduction to Reinforcement Learning (I)

Distingushing Cause from Effect Using Observational Data: Methods and Benchmarks (M. Mooij et al.)

Estimating Causal Structure Using Conditional DAG Model (by Chris. J. Oates et al.)

Conditional distribution variability measures for causality detection (by Jose A. R. fonollosa)

Dropout: A Simple Way to Prevent Neural Networks from Overfitting, Nitish Srivastava et al.

Transfer Metric Learning by Learning Task Relationships

Introduction to Reinforcement Learning (end)

Deep Residual Learning for image recognition, Kaiming He et al. (2015)

Mastering the game of Go with deep neural networks and tree search, D. Silver et al. (2016)

Human-level control through deep reinforcement learning, V. Mnih et al. (2015)

Recovering from Selection Bias in Causal and Statistical Inference, E. Bareinboim et. al. (2014)

Regularization of Neural Networks using DropConnect, Li Wan et al. 2013

Image Style Transfer Using Convolutional Neural Networks (CVPR 2016)

- Sequential Model-Based Optimization for General Algorithm Configuration by F. Hutter et al.

- Efficient Robust Automated Machine Learning by M. Feurer et al.

Could a Neuroscientist Understand a Microprocessor? by Eric Jonas and Konrad Paul Kording

Martin Arjovsky and Leon Bottou. Towards Principled Methods for Training Generative Adversarial Networks, January 2017.

Generative Adversarial Networks, Ian J. Goodfellow et al.

"DeepStack: Expert-Level Artificial Intelligence in Heads-Up No-Limit Poker" by Matej Moravčík, Martin Schmid

Tangent Prop - A Formalism for Specifying Selected

Invariances in an Adaptive Network (1991)

Learning a Parametric Embedding by Preserving Local Structure

PathNet: Evolution Channels Gradient Descent in Super Neural Networks (by Chrisantha Fernando et al. 2017)

Extended Topological Metrics for the Analysis of Power Grid Vulnerability (by E. Bompard, E. Pons and D. Wu? 2012)

Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances (by Marco Cuturi, 2013)

Memory-efficient Backpropagation Through Time by Audrunas Gruslys et al. (2016)

Understanding deep learning requires rethinking generalization by C. Zhang et al. (ICLR 2017)

Learning to act by predicting the future, Alexey Dosovitskiy (ICLR 2017)

- Neural Architecture Search with Reinforcement Learning by Barret Aoph and Quoc V. Le (ICLR 2017)

- Designing Neural Network Architectures using Reinforcement Learning by B. Baker et al. (ICLR 2017)

Entropy-SGD : Biasing gradient descent into wide valleys by P. Chaudhari et al. (ICLR 2017)

Latent Dirichlet Allocation, David M. Blei et al. (JMLR 2003)