Publication

Variable Computation in Recurrent Neural Networks

International Conference on Learning Representations (ICLR) 2017


Abstract

Recurrent neural networks (RNNs) have been used extensively and with increasing success to model various types of sequential data. Much of this progress has been achieved through devising recurrent units and architectures with the flexibility to capture complex statistics in the data, such as long range dependency or localized attention phenomena. However, while many sequential data (such as video, speech or language) can have highly variable information flow, most recurrent models still consume input features at a constant rate and perform a constant number of computations per time step, which can be detrimental to both speed and model capacity. 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. We show experimentally that not only do our models require fewer operations, they also lead to better performance overall on evaluation tasks.

Related Publications

All Publications

A hierarchical loss and its problems when classifying non-hierarchically

Cinna Wu, Mark Tygert, Yann LeCun

PLOS ONE - December 3, 2019

Neural Supersampling for Real-time Rendering

Lei Xiao, Salah Nouri, Matt Chapman, Alexander Fix, Douglas Lanman, Anton Kaplanyan

ACM SIGGRAPH - August 17, 2020

CamemBERT: a Tasty French Language Model

Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah, Benoît Sagot

ACL - June 21, 2020

Don’t Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training

Margaret Li, Stephen Roller, Ilia Kulikov, Sean Welleck, Y-Lan Boureau, Kyunghyun Cho, Jason Weston

ACL - June 22, 2020

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy