All Research Areas
Research Areas
Year Published

129 Results

April 24, 2017

Towards Principled Methods for Training Generative Adversarial Networks

International Conference on Learning Representations (ICLR) 2017

The goal of this paper is not to introduce a single algorithm or method, but to make theoretical steps towards fully understanding the training dynamics of generative adversarial networks.

By: Leon Bottou, Martin Arjovsky
April 24, 2017

Improving Neural Language Models with a Continuous Cache

International Conference on Learning Representations (ICLR) 2017

We propose an extension to neural network language models to adapt their prediction to the recent history. Our model is a simplified version of memory augmented networks, which stores past hidden activations as memory and accesses them through a dot product with the current hidden activation.

By: Armand Joulin, Edouard Grave, Nicolas Usunier
April 24, 2017

Revisiting Classifier Two-Sample Tests for GAN Evaluation and Causal Discovery

International Conference on Learning Representations (ICLR) 2017

In this paper, we aim to revive interest in the use of binary classifiers for two-sample testing. To this end, we review their fundamentals, previous literature on their use, compare their performance against alternative state-of-the-art two-sample tests, and propose them to evaluate generative adversarial network models applied to image synthesis.

By: David Lopez-Paz, Maxime Oquab
April 24, 2017

CommAI: Evaluating the First Steps Towards a Useful General AI

ICLR 2017 Workshop

We propose a set of concrete desiderata for general AI, together with a platform to test machines on how well they satisfy such desiderata, while keeping all further complexities to a minimum.

By: Marco Baroni, Armand Joulin, Allan Jabri, Germán Kruszewski, Angeliki Lazaridou, Klemen Simonic, Tomas Mikolov
April 24, 2017

Automatic Rule Extraction from Long Short Term Memory Networks

International Conference on Learning Representations (ICLR) 2017

In this paper we consider Long Short Term Memory networks (LSTMs) and demonstrate a new approach for tracking the importance of a given input to the LSTM for a given output.

By: W. James Murdoch, Arthur Szlam
April 24, 2017

Learning End-to-End Goal-Oriented Dialog

International Conference on Learning Representations (ICLR)

This paper proposes a testbed to break down the strengths and shortcomings of end-to-end dialog systems in goal-oriented applications.

By: Antoine Bordes, Y-Lan Boureau, Jason Weston
April 24, 2017

Variable Computation in Recurrent Neural Networks

International Conference on Learning Representations (ICLR) 2017

In this paper, we explore a modification to existing recurrent units which allows them to learn to vary the amount of computation they perform at each step, without prior knowledge of the sequence’s time structure.

By: Yacine Jernite, Edouard Grave, Armand Joulin, Tomas Mikolov
April 24, 2017

Tracking the World State with Recurrent Entity Networks

International Conference on Learning Representations (ICLR) 2017

We introduce a new model, the Recurrent Entity Network (EntNet). It is equipped with a dynamic long-term memory which allows it to maintain and update a representation of the state of the world as it receives new data.

By: Mikael Henaff, Jason Weston, Arthur Szlam, Antoine Bordes, Yann LeCun
April 24, 2017

Episodic Exploration for Deep Deterministic Policies for StarCraft Micro-Management

International Conference on Learning Representations (ICLR) 2017

We consider scenarios from the real-time strategy game StarCraft as benchmarks for reinforcement learning algorithms.

By: Gabriel Synnaeve, Zeming Lin, Soumith Chintala
April 24, 2017

Multi-Agent Cooperation and the Emergence of (Natural) Language

International Conference on Learning Representations (ICLR) 2017

This paper proposes a framework for language learning that relies on multi-agent communication.

By: Angeliki Lazaridou, Alexander Peysakhovich, Marco Baroni