Research Area
Year Published

219 Results

June 10, 2019

Making Deep Q-learning Methods Robust to Time Discretization

International Conference on Machine Learning (ICML)

Despite remarkable successes, Deep Reinforcement Learning (DRL) is not robust to hyperparameterization, implementation details, or small environment changes (Henderson et al. 2017, Zhang et al. 2018). Overcoming such sensitivity is key to making DRL applicable to real world problems. In this paper, we identify sensitivity to time discretization in near continuous-time environments as a critical factor; this covers, e.g., changing the number of frames per second, or the action frequency of the controller.

By: Corentin Tallec, Léonard Blier, Yann Ollivier

June 7, 2019

Cycle-Consistency for Robust Visual Question Answering

Computer Vision and Pattern Recognition (CVPR)

Despite significant progress in Visual Question Answering over the years, robustness of today’s VQA models leave much to be desired. We introduce a new evaluation protocol and associated dataset (VQA-Rephrasings) and show that state-of-the-art VQA models are notoriously brittle to linguistic variations in questions.

By: Meet Shah, Xinlei Chen, Marcus Rohrbach, Devi Parikh

June 4, 2019

Deep Counterfactual Regret Minimization

International Conference on Machine Learning (ICML)

Counterfactual Regret Minimization (CFR) is the leading framework for solving large imperfect-information games. It converges to an equilibrium by iteratively traversing the game tree. In order to deal with extremely large games, abstraction is typically applied before running CFR. The abstracted game is solved with tabular CFR, and its solution is mapped back to the full game.

By: Noam Brown, Adam Lerer, Sam Gross, Tuomas Sandholm

June 3, 2019

Pay less attention with Lightweight and Dynamic Convolutions

International Conference on Learning Representations (ICLR)

Self-attention is a useful mechanism to build generative models for language and images. It determines the importance of context elements by comparing each element to the current time step. In this paper, we show that a very lightweight convolution can perform competitively to the best reported self-attention results.

By: Felix Wu, Angela Fan, Alexei Baevski, Yann Dauphin, Michael Auli

June 2, 2019

The emergence of number and syntax units in LSTM language models

Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)

We present here a detailed study of the inner mechanics of number tracking in LSTMs at the single neuron level. We discover that long-distance number information is largely managed by two “number units”.

By: Yair Lakretz, Germán Kruszewski, Theo Desbordes, Dieuwke Hupkes, Stanislas Dehaene, Marco Baroni

May 31, 2019

Abusive Language Detection with Graph Convolutional Networks

North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)

Abuse on the Internet represents a significant societal problem of our time. Previous research on automated abusive language detection in Twitter has shown that community-based profiling of users is a promising technique for this task. However, existing approaches only capture shallow properties of online communities by modeling follower–following relationships.

By: Pushkar Mishra, Marco Del Tredici, Helen Yannakoudakis, Ekaterina Shutova

May 31, 2019

Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity

Journal of Artificial Intelligence Research

In this paper, we introduce proximal gradient temporal difference learning, which provides a principled way of designing and analyzing true stochastic gradient temporal difference learning algorithms.

By: Bo Liu, Ian Gemp, Mohammad Ghavamzadeh, Ji Liu, Sridhar Mahadevan, Marek Petrik

May 17, 2019

Unsupervised Hyper-alignment for Multilingual Word Embeddings

International Conference on Learning Representations (ICLR)

We consider the problem of aligning continuous word representations, learned in multiple languages, to a common space. It was recently shown that, in the case of two languages, it is possible to learn such a mapping without supervision. This paper extends this line of work to the problem of aligning multiple languages to a common space.

By: Jean Alaux, Edouard Grave, Marco Cuturi, Armand Joulin

May 15, 2019

Towards Unsupervised Single-Channel Blind Source Separation using Adversarial Pair Unmix-and-Remix

International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Blind single-channel source separation is a long standing signal processing challenge. Many methods were proposed to solve this task utilizing multiple signal priors such as low rank, sparsity, temporal continuity etc. The recent advance of generative adversarial models presented new opportunities in signal regression tasks. The power of adversarial training however has not yet been realized for blind source separation tasks.

By: Yedid Hoshen

May 14, 2019

Online Learning for Measuring Incentive Compatibility in Ad Auctions

The Web Conference

In this paper we investigate the problem of measuring end-to-end Incentive Compatibility (IC) regret given black-box access to an auction mechanism. Our goal is to 1) compute an estimate for IC regret in an auction, 2) provide a measure of certainty around the estimate of IC regret, and 3) minimize the time it takes to arrive at an accurate estimate.

By: Zhe Feng, Okke Schrijvers, Eric Sodomka