| Exploratory combinatorial optimization with reinforcement learning T Barrett, W Clements, J Foerster, A Lvovsky Proceedings of the AAAI conference on artificial intelligence 34 (04), 3243-3250, 2020 | 235 | 2020 |
| Backpropagation through nonlinear units for the all-optical training of neural networks X Guo*, TD Barrett*, ZM Wang, AI Lvovsky Photonics Research 9 (3), B71-B80, 2021 | 85 | 2021 |
| Fully reconfigurable coherent optical vector–matrix multiplication J Spall, X Guo, TD Barrett, AI Lvovsky Optics Letters 45 (20), 5752-5755, 2020 | 82 | 2020 |
| Autoregressive neural-network wavefunctions for ab initio quantum chemistry TD Barrett, A Malyshev, AI Lvovsky Nature Machine Intelligence 4 (4), 351-358, 2022 | 75 | 2022 |
| Learning disentangled representations and group structure of dynamical environments R Quessard*, T Barrett*, W Clements* Advances in Neural Information Processing Systems 33, 19727-19737, 2020 | 72* | 2020 |
| Winner takes it all: Training performant RL populations for combinatorial optimization N Grinsztajn, D Furelos-Blanco, S Surana, C Bonnet, T Barrett Advances in Neural Information Processing Systems 36, 48485-48509, 2023 | 40* | 2023 |
| Reinforcement learning for branch-and-bound optimisation using retrospective trajectories CWF Parsonson, A Laterre, TD Barrett Proceedings of the AAAI Conference on Artificial Intelligence 37 (4), 4061-4069, 2023 | 28 | 2023 |
| Pushing Purcell enhancement beyond its limits TD Barrett, TH Doherty, A Kuhn New Journal of Physics 22 (6), 063013, 2020 | 24 | 2020 |
| Combinatorial optimization with policy adaptation using latent space search F Chalumeau, S Surana, C Bonnet, N Grinsztajn, A Pretorius, A Laterre, ... Advances in Neural Information Processing Systems 36, 2024 | 23 | 2024 |
| Nonlinear Zeeman effects in the cavity-enhanced emission of polarised photons TD Barrett, D Stuart, O Barter, A Kuhn New Journal of Physics 20 (7), 073030, 2018 | 18 | 2018 |
| Polarization oscillations in birefringent emitter-cavity systems TD Barrett, O Barter, D Stuart, B Yuen, A Kuhn Physical Review Letters 122 (8), 083602, 2019 | 17 | 2019 |
| Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs AP Smit, N Grinsztajn, P Duckworth, TD Barrett, A Pretorius Forty-first International Conference on Machine Learning, 0 | 12* | |
| Learning to solve combinatorial graph partitioning problems via efficient exploration TD Barrett, CWF Parsonson, A Laterre arXiv preprint arXiv:2205.14105, 2022 | 11 | 2022 |
| How to administer an antidote to Schrödinger’s cat JR Álvarez, M IJspeert, O Barter, B Yuen, TD Barrett, D Stuart, J Dilley, ... Journal of Physics B: Atomic, Molecular and Optical Physics 55 (5), 054001, 2022 | 9 | 2022 |
| Universally expressive communication in multi-agent reinforcement learning M Morris, TD Barrett, A Pretorius Advances in Neural Information Processing Systems 35, 33508-33522, 2022 | 8 | 2022 |
| Multimode interferometry for entangling atoms in quantum networks TD Barrett, A Rubenok, D Stuart, O Barter, A Holleczek, J Dilley, ... Quantum Science and Technology 4 (2), 025008, 2019 | 8 | 2019 |
| So manyfolds, so little time: efficient protein structure prediction with pLMs and MSAs TD Barrett, A Villegas-Morcillo, L Robinson, B Gaujac, D Adméte, ... bioRxiv, 2022.10. 15.511553, 2022 | 6 | 2022 |
| Jumanji: Industry-Driven Hardware-Accelerated RL Environments.(2022) C Bonnet, D Byrne, V Le, L Midgley, D Luo, C Waters, S Abramowitz, ... URL https://github. com/instadeepai/jumanji, 2022 | 6 | 2022 |
| Contrasting Sequence with Structure: Pre-training Graph Representations with PLMs L Robinson, T Atkinson, L Copoiu, P Bordes, T Pierrot, TD Barrett bioRxiv, 2023.12. 01.569611, 2023 | 5 | 2023 |
| One step at a time: Pros and cons of multi-step meta-gradient reinforcement learning C Bonnet, P Caron, T Barrett, I Davies, A Laterre arXiv preprint arXiv:2111.00206, 2021 | 5 | 2021 |
A.I. LvovskyUniversity of OxfordVerified email at physics.ox.ac.uk
