Tom Le Paine

According to our database, Tom Le Paine authored at least 20 papers between 2015 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Reinforced Self-Training (ReST) for Language Modeling.

Çaglar Gülçehre, Tom Le Paine, Srivatsan Srinivasan, Ksenia Konyushkova, Lotte Weerts, Abhishek Sharma, Aditya Siddhant, Alex Ahern, Miaosen Wang, Chenjie Gu, Wolfgang Macherey, Arnaud Doucet, Orhan Firat, Nando de Freitas

CoRR, 2023

AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning.

Michaël Mathieu, Sherjil Ozair, Srivatsan Srinivasan, Çaglar Gülçehre, Shangtong Zhang, Ray Jiang, Tom Le Paine, Richard Powell, Konrad Zolna, Julian Schrittwieser, David H. Choi, Petko Georgiev, Daniel Toyama, Aja Huang, Roman Ring, Igor Babuschkin, Timo Ewalds, Mahyar Bordbar, Sarah Henderson, Sergio Gómez Colmenarejo, Aäron van den Oord, Wojciech Marian Czarnecki, Nando de Freitas, Oriol Vinyals

CoRR, 2023

π2vec: Policy Representations with Successor Features.

Gianluca Scarpellini, Ksenia Konyushkova, Claudio Fantacci, Tom Le Paine, Yutian Chen, Misha Denil

CoRR, 2023

2022

On Instrumental Variable Regression for Deep Offline Policy Evaluation.

Yutian Chen, Liyuan Xu, Çaglar Gülçehre, Tom Le Paine, Arthur Gretton, Nando de Freitas, Arnaud Doucet

J. Mach. Learn. Res., 2022

2021

Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization.

Michael R. Zhang, Tom Le Paine, Ofir Nachum, Cosmin Paduraru, George Tucker, Ziyu Wang, Mohammad Norouzi

CoRR, 2021

Benchmarks for Deep Off-Policy Evaluation.

Justin Fu, Mohammad Norouzi, Ofir Nachum, George Tucker, Ziyu Wang, Alexander Novikov, Mengjiao Yang, Michael R. Zhang, Yutian Chen, Aviral Kumar, Cosmin Paduraru, Sergey Levine, Tom Le Paine

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Hyperparameter Selection for Offline Reinforcement Learning.

Tom Le Paine, Cosmin Paduraru, Andrea Michi, Çaglar Gülçehre, Konrad Zolna, Alexander Novikov, Ziyu Wang, Nando de Freitas

CoRR, 2020

RL Unplugged: Benchmarks for Offline Reinforcement Learning.

Çaglar Gülçehre, Ziyu Wang, Alexander Novikov, Tom Le Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matt Hoffman, Ofir Nachum, George Tucker, Nicolas Heess, Nando de Freitas

CoRR, 2020

Acme: A Research Framework for Distributed Reinforcement Learning.

Matt Hoffman, Bobak Shahriari, John Aslanides, Gabriel Barth-Maron, Feryal M. P. Behbahani, Tamara Norman, Abbas Abdolmaleki, Albin Cassirer, Fan Yang, Kate Baumli, Sarah Henderson, Alexander Novikov, Sergio Gómez Colmenarejo, Serkan Cabi, Çaglar Gülçehre, Tom Le Paine, Andrew Cowie, Ziyu Wang, Bilal Piot, Nando de Freitas

CoRR, 2020

Making Efficient Use of Demonstrations to Solve Hard Exploration Problems.

Çaglar Gülçehre, Tom Le Paine, Bobak Shahriari, Misha Denil, Matt Hoffman, Hubert Soyer, Richard Tanburn, Steven Kapturowski, Neil C. Rabinowitz, Duncan Williams, Gabriel Barth-Maron, Ziyu Wang, Nando de Freitas, Worlds Team

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Grandmaster level in StarCraft II using multi-agent reinforcement learning.

Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H. Choi, Richard Powell, Timo Ewalds, Petko Georgiev, Junhyuk Oh, Dan Horgan, Manuel Kroiss, Ivo Danihelka, Aja Huang, Laurent Sifre, Trevor Cai, John P. Agapiou, Max Jaderberg, Alexander Sasha Vezhnevets, Rémi Leblond, Tobias Pohlen, Valentin Dalibard, David Budden, Yury Sulsky, James Molloy, Tom Le Paine, Çaglar Gülçehre, Ziyu Wang, Tobias Pfaff, Yuhuai Wu, Roman Ring, Dani Yogatama, Dario Wünsch, Katrina McKinney, Oliver Smith, Tom Schaul, Timothy P. Lillicrap, Koray Kavukcuoglu, Demis Hassabis, Chris Apps, David Silver

Nat., 2019

Improving the Gating Mechanism of Recurrent Neural Networks.

Albert Gu, Çaglar Gülçehre, Tom Le Paine, Matthew W. Hoffman, Razvan Pascanu

CoRR, 2019

2018

One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL.

Tom Le Paine, Sergio Gomez Colmenarejo, Ziyu Wang, Scott E. Reed, Yusuf Aytar, Tobias Pfaff, Matthew W. Hoffman, Gabriel Barth-Maron, Serkan Cabi, David Budden, Nando de Freitas

CoRR, 2018

Playing hard exploration games by watching YouTube.

Yusuf Aytar, Tobias Pfaff, David Budden, Tom Le Paine, Ziyu Wang, Nando de Freitas

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Fast Generation for Convolutional Autoregressive Models.

Prajit Ramachandran, Tom Le Paine, Pooya Khorrami, Mohammad Babaeizadeh, Shiyu Chang, Yang Zhang, Mark A. Hasegawa-Johnson, Roy H. Campbell, Thomas S. Huang

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Fast Wavenet Generation Algorithm.

Tom Le Paine, Pooya Khorrami, Shiyu Chang, Yang Zhang, Prajit Ramachandran, Mark A. Hasegawa-Johnson, Thomas S. Huang

CoRR, 2016

Seq-NMS for Video Object Detection.

Wei Han, Pooya Khorrami, Tom Le Paine, Prajit Ramachandran, Mohammad Babaeizadeh, Honghui Shi, Jianan Li, Shuicheng Yan, Thomas S. Huang

CoRR, 2016

How deep neural networks can improve emotion recognition on video data.

Pooya Khorrami, Tom Le Paine, Kevin Brady, Charlie K. Dagli, Thomas S. Huang

Proceedings of the 2016 IEEE International Conference on Image Processing, 2016

2015

An Analysis of Unsupervised Pre-training in Light of Recent Advances.

Tom Le Paine, Pooya Khorrami, Wei Han, Thomas S. Huang

Proceedings of the 3rd International Conference on Learning Representations, 2015

Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition?

Pooya Khorrami, Tom Le Paine, Thomas S. Huang

Proceedings of the 2015 IEEE International Conference on Computer Vision Workshop, 2015