April 24, 2017

FAIR research being presented at ICLR 2017

By: Facebook Research

Facebook AI (FAIR) researchers and engineers are converging at the 5th annual International Conference on Learning Representations (ICLR) 2017 in Toulon, France this week. ICLR brings together the top artificial intelligence and machine learning experts to discuss how to best learn meaningful and useful representations of data to application areas such as vision, speech, audio and natural language processing.

At ICLR they will be sharing their latest research in AI through 17 conference and workshop track papers, including a subset of their work on dialog systems which is outlined here.  The complete list of FAIR research papers being presented at ICLR is:

An Analytical Formula of Population Gradient for Two-Layered ReLU network and its Applications in Convergence and Critical Point Analysis
Yuandong Tian

Automatic Rule Extraction from Long Short Term Memory Networks
James Murdoch and Arthur Szlam

CommAI: Evaluating the Frst Steps Towards a Useful General AI
Marco Baroni, Armand Joulin, Allan Jabri, Germaan Kruszewski, Angeliki Lazaridou, Klemen Simonic, and Tomas Mikolov

Dialogue Learning With Human-in-the-Loop
Jiwei Li, Alexander H. Miller, Sumit Chopra, Marc’Aurelio Ranzato, and Jason Weston

DSD: Dense-Sparse-Dense Training for Deep Neural Networks
Song Han, Jeff Pool, Sharan Narang, Huizi Mao, Enhao Gong, Shijian Tang, Erich Elsen, Peter Vajda, Manohar Paluri, John Tran, Bryan Catanzaro, William J. Dally

Episodic Exploration for Deep Deterministic Policies for StarCraft Micromanagement
Nicolas Usunier, Gabriel Synnaeve, Zeming Lin, and Soumith Chintala

Improving Neural Language Models with a Continuous Cache
Edouard Grave, Armand Joulin, and Nicolas Usunier

Learning End-to-End Goal-Oriented Dialog
Antoine Bordes, Y-Lan Boureau, and Jason Weston

Learning Through Dialogue Interactions by Asking Questions
Jiwei Li, Alexander H. Miller, Sumit Chopra, Marc’Aurelio Ranzato, and Jason Weston

LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation
Jianwei Yang, Anitha Kannan, Dhruv Batra, and Devi Parikh

Multi-Agent Cooperation and the Emergence of (Natural) Language
Angeliki Lazaridou, Alexander Peysakhovich, and Marco Baroni

Revisiting Classifier Two-Sample Tests
David Lopez-Paz and Maxime Oquab

Towards Principled Methods for Training Generative Adversarial Networks
Martin Arjovsky and Leon Bottou

Tracking the World State with Recurrent Entity Networks
Mikael Henaff, Jason Weston, Arthur Szlam, Antoine Bordes, and Yann LeCun

Training Agent for First-Person Shooter Game with Actor-Critic Curriculum Learning
Yuxin Wu and Yuandong Tian

Unsupervised Cross-Domain Image Generation
Yaniv Taigman, Adam Polyak, and Lior Wolf

Variable Computation in Recurrent Neural Networks
Yacine Jernite, Edouard Grave, Armand Joulin, and Tomas Mikolov