Thomas D. Barrett

According to our database, Thomas D. Barrett authored at least 17 papers between 2019 and 2024.

Collaborative distances:

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

2024

Metalic: Meta-Learning In-Context with Protein Language Models.

Jacob Beck, Shikha Surana, Manus McAuliffe, Oliver Bent, Thomas D. Barrett, Juan Jose Garau Luis, Paul Duckworth

CoRR, 2024

Memory-Enhanced Neural Solvers for Efficient Adaptation in Combinatorial Optimization.

Félix Chalumeau, Refiloe Shabe, Noah de Nicola, Arnu Pretorius, Thomas D. Barrett, Nathan Grinsztajn

CoRR, 2024

Learning the Language of Protein Structure.

Benoit Gaujac, Jérémie Donà, Liviu Copoiu, Timothy Atkinson, Thomas Pierrot, Thomas D. Barrett

CoRR, 2024

Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs.

Andries P. Smit, Nathan Grinsztajn, Paul Duckworth, Thomas D. Barrett, Arnu Pretorius

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

ManyFold: an efficient and flexible library for training and validating protein folding models.

Amelia Villegas-Morcillo, Louis Robinson, Arthur Flajolet, Thomas D. Barrett

Bioinform., January, 2023

Are we going MAD? Benchmarking Multi-Agent Debate between Language Models for Medical Q&A.

Andries P. Smit, Paul Duckworth, Nathan Grinsztajn, Kale-ab Tessera, Thomas D. Barrett, Arnu Pretorius

CoRR, 2023

Combinatorial Optimization with Policy Adaptation using Latent Space Search.

Félix Chalumeau, Shikha Surana, Clément Bonnet, Nathan Grinsztajn, Arnu Pretorius, Alexandre Laterre, Thomas D. Barrett

CoRR, 2023

Reinforcement Learning for Branch-and-Bound Optimisation Using Retrospective Trajectories.

Christopher W. F. Parsonson, Alexandre Laterre, Thomas D. Barrett

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Autoregressive neural-network wavefunctions for ab initio quantum chemistry.

Thomas D. Barrett, Aleksei O. Malyshev, A. I. Lvovsky

Nat. Mach. Intell., 2022

Population-Based Reinforcement Learning for Combinatorial Optimization.

Nathan Grinsztajn, Daniel Furelos-Blanco, Thomas D. Barrett

CoRR, 2022

Learning to Solve Combinatorial Graph Partitioning Problems via Efficient Exploration.

Thomas D. Barrett, Christopher W. F. Parsonson, Alexandre Laterre

CoRR, 2022

Universally Expressive Communication in Multi-Agent Reinforcement Learning.

Matthew Morris, Thomas D. Barrett, Arnu Pretorius

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

One Step at a Time: Pros and Cons of Multi-Step Meta-Gradient Reinforcement Learning.

Clément Bonnet, Paul Caron, Thomas D. Barrett, Ian Davies, Alexandre Laterre

CoRR, 2021

2020

Learning Group Structure and Disentangled Representations of Dynamical Environments.

Robin Quessard, Thomas D. Barrett, William R. Clements

CoRR, 2020

Learning Disentangled Representations and Group Structure of Dynamical Environments.

Robin Quessard, Thomas D. Barrett, William R. Clements

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Exploratory Combinatorial Optimization with Reinforcement Learning.

Thomas D. Barrett, William R. Clements, Jakob N. Foerster, A. I. Lvovsky

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

End-to-end optical backpropagation for training neural networks.

Xianxin Guo, Thomas D. Barrett, Zhiming M. Wang, A. I. Lvovsky

CoRR, 2019