activity
20182022
most citedTorchBeast: A PyTorch Platform for Distributed RL

27 citations · 52 across the 2 of their papers we have counts for

collaborators

9 papers

cs.CL202225 cited

HyperTree Proof Search for Neural Theorem Proving

Guillaume Lample, Marie-Anne Lachaux, Thibaut Lavril +5

We propose an online training procedure for a transformer-based automated theorem prover. Our approach leverages a new search algorithm, HyperTree Proof Search (HTPS), inspired by…

cond-mat.mtrl-sci2020

An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage

C. Lawrence Zitnick, Lowik Chanussot, Abhishek Das +14

Scalable and cost-effective solutions to renewable energy storage are essential to addressing the world's rising energy needs while reducing climate change. As we increase our reli…

cond-mat.mtrl-sci2020

The Open Catalyst 2020 (OC20) Dataset and Community Challenges

Lowik Chanussot, Abhishek Das, Siddharth Goyal +14

Catalyst discovery and optimization is key to solving many societal and energy challenges including solar fuels synthesis, long-term energy storage, and renewable fertilizer produc…

cs.LG2020

Addressing Some Limitations of Transformers with Feedback Memory

Angela Fan, Thibaut Lavril, Edouard Grave +2

Transformers have been successfully applied to sequential, auto-regressive tasks despite being feedforward networks. Unlike recurrent neural networks, Transformers use attention to…

cs.LG201927 cited

TorchBeast: A PyTorch Platform for Distributed RL

Heinrich Küttler, Nantas Nardelli, Thibaut Lavril +4

TorchBeast is a platform for reinforcement learning (RL) research in PyTorch. It implements a version of the popular IMPALA algorithm for fast, asynchronous, parallel training of R…

cs.LG2018

Efficient keyword spotting using dilated convolutions and gating

Alice Coucke, Mohammed Chlieh, Thibault Gisselbrecht +3

We explore the application of end-to-end stateless temporal modeling to small-footprint keyword spotting as opposed to recurrent networks that model long-term temporal dependencies…