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2020 – today
- 2024
[c5]Andries P. Smit, Nathan Grinsztajn, Paul Duckworth, Thomas D. Barrett, Arnu Pretorius:
Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs. ICML 2024- [i13]Benoit Gaujac, Jérémie Donà, Liviu Copoiu, Timothy Atkinson, Thomas Pierrot, Thomas D. Barrett:
Learning the Language of Protein Structure. CoRR abs/2405.15840 (2024) - [i12]Félix Chalumeau, Refiloe Shabe, Noah de Nicola, Arnu Pretorius, Thomas D. Barrett, Nathan Grinsztajn:
Memory-Enhanced Neural Solvers for Efficient Adaptation in Combinatorial Optimization. CoRR abs/2406.16424 (2024) - 2023
- [j2]Amelia Villegas-Morcillo
, Louis Robinson, Arthur Flajolet, Thomas D. Barrett:
ManyFold: an efficient and flexible library for training and validating protein folding models. Bioinform. 39(1) (2023) - [c4]Christopher W. F. Parsonson, Alexandre Laterre, Thomas D. Barrett:
Reinforcement Learning for Branch-and-Bound Optimisation Using Retrospective Trajectories. AAAI 2023: 4061-4069 - [i11]Félix Chalumeau, Shikha Surana, Clément Bonnet, Nathan Grinsztajn, Arnu Pretorius, Alexandre Laterre, Thomas D. Barrett:
Combinatorial Optimization with Policy Adaptation using Latent Space Search. CoRR abs/2311.13569 (2023) - [i10]Andries P. Smit, Paul Duckworth, Nathan Grinsztajn, Kale-ab Tessera, Thomas D. Barrett, Arnu Pretorius:
Are we going MAD? Benchmarking Multi-Agent Debate between Language Models for Medical Q&A. CoRR abs/2311.17371 (2023) - 2022
- [j1]Thomas D. Barrett, Aleksei O. Malyshev, A. I. Lvovsky:
Autoregressive neural-network wavefunctions for ab initio quantum chemistry. Nat. Mach. Intell. 4(4): 351-358 (2022) - [c3]Matthew Morris, Thomas D. Barrett, Arnu Pretorius:
Universally Expressive Communication in Multi-Agent Reinforcement Learning. NeurIPS 2022 - [i9]Thomas D. Barrett, Christopher W. F. Parsonson, Alexandre Laterre:
Learning to Solve Combinatorial Graph Partitioning Problems via Efficient Exploration. CoRR abs/2205.14105 (2022) - [i8]Christopher W. F. Parsonson, Alexandre Laterre, Thomas D. Barrett:
Reinforcement Learning for Branch-and-Bound Optimisation using Retrospective Trajectories. CoRR abs/2205.14345 (2022) - [i7]Matthew Morris, Thomas D. Barrett, Arnu Pretorius:
Universally Expressive Communication in Multi-Agent Reinforcement Learning. CoRR abs/2206.06758 (2022) - [i6]Nathan Grinsztajn, Daniel Furelos-Blanco, Thomas D. Barrett:
Population-Based Reinforcement Learning for Combinatorial Optimization. CoRR abs/2210.03475 (2022) - 2021
- [i5]Thomas D. Barrett, Aleksei O. Malyshev, A. I. Lvovsky:
Autoregressive neural-network wavefunctions for ab initio quantum chemistry. CoRR abs/2109.12606 (2021) - [i4]Clément Bonnet, Paul Caron, Thomas D. Barrett, Ian Davies, Alexandre Laterre:
One Step at a Time: Pros and Cons of Multi-Step Meta-Gradient Reinforcement Learning. CoRR abs/2111.00206 (2021) - 2020
- [c2]Thomas D. Barrett, William R. Clements, Jakob N. Foerster, A. I. Lvovsky:
Exploratory Combinatorial Optimization with Reinforcement Learning. AAAI 2020: 3243-3250 - [c1]Robin Quessard, Thomas D. Barrett, William R. Clements:
Learning Disentangled Representations and Group Structure of Dynamical Environments. NeurIPS 2020 - [i3]Robin Quessard, Thomas D. Barrett, William R. Clements:
Learning Group Structure and Disentangled Representations of Dynamical Environments. CoRR abs/2002.06991 (2020)
2010 – 2019
- 2019
- [i2]Thomas D. Barrett, William R. Clements, Jakob N. Foerster, A. I. Lvovsky:
Exploratory Combinatorial Optimization with Reinforcement Learning. CoRR abs/1909.04063 (2019) - [i1]Xianxin Guo, Thomas D. Barrett, Zhiming M. Wang, A. I. Lvovsky:
End-to-end optical backpropagation for training neural networks. CoRR abs/1912.12256 (2019)
Coauthor Index
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