Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 3308 | 2023 |
Value-decomposition networks for cooperative multi-agent learning P Sunehag, G Lever, A Gruslys, WM Czarnecki, V Zambaldi, M Jaderberg, ... arXiv preprint arXiv:1706.05296, 2017 | 2082 | 2017 |
Human-level performance in 3D multiplayer games with population-based reinforcement learning M Jaderberg, WM Czarnecki, I Dunning, L Marris, G Lever, AG Castaneda, ... Science 364 (6443), 859-865, 2019 | 884 | 2019 |
Solving mixed integer programs using neural networks V Nair, S Bartunov, F Gimeno, I Von Glehn, P Lichocki, I Lobov, ... arXiv preprint arXiv:2012.13349, 2020 | 327 | 2020 |
Deep reinforcement learning and the deadly triad H Van Hasselt, Y Doron, F Strub, M Hessel, N Sonnerat, J Modayil arXiv preprint arXiv:1812.02648, 2018 | 284 | 2018 |
Human-level performance in first-person multiplayer games with population-based deep reinforcement learning M Jaderberg, WM Czarnecki, I Dunning, L Marris, G Lever, AG Castaneda, ... arXiv preprint arXiv:1807.01281, 2018 | 177 | 2018 |
Scan: Learning hierarchical compositional visual concepts I Higgins, N Sonnerat, L Matthey, A Pal, CP Burgess, M Bosnjak, ... arXiv preprint arXiv:1707.03389, 2017 | 146 | 2017 |
Gemma scope: Open sparse autoencoders everywhere all at once on gemma 2 T Lieberum, S Rajamanoharan, A Conmy, L Smith, N Sonnerat, V Varma, ... arXiv preprint arXiv:2408.05147, 2024 | 65 | 2024 |
Learning a large neighborhood search algorithm for mixed integer programs N Sonnerat, P Wang, I Ktena, S Bartunov, V Nair arXiv preprint arXiv:2107.10201, 2021 | 59 | 2021 |
Value-decomposition networks for cooperative multi-agent learning. arXiv 2017 P Sunehag, G Lever, A Gruslys, WM Czarnecki, V Zambaldi, M Jaderberg, ... arXiv preprint arXiv:1706.05296, 2017 | 43 | 2017 |
Jumping ahead: Improving reconstruction fidelity with jumprelu sparse autoencoders S Rajamanoharan, T Lieberum, N Sonnerat, A Conmy, V Varma, J Kramár, ... arXiv preprint arXiv:2407.14435, 2024 | 35 | 2024 |
Scan: learning abstract hierarchical compositional visual concepts I Higgins, N Sonnerat, L Matthey, A Pal, CP Burgess, M Botvinick, ... arXiv preprint arXiv:1707.03389, 2017 | 29 | 2017 |
Learning visual concepts using neural networks A Lerchner, I Higgins, N Sonnerat, AT Pal, D Hassabis, ... US Patent 11,354,823, 2022 | 14 | 2022 |
Maximum flows on disjoint paths G Naves, N Sonnerat, A Vetta International Workshop on Randomization and Approximation Techniques in …, 2010 | 6 | 2010 |
Finding increasingly large extremal graphs with alphazero and tabu search A Mehrabian, A Anand, H Kim, N Sonnerat, M Balog, G Comanici, ... arXiv preprint arXiv:2311.03583, 2023 | 5 | 2023 |
Galaxy cutsets in graphs N Sonnerat, A Vetta Journal of combinatorial optimization 19, 415-427, 2010 | 3 | 2010 |
Defending planar graphs against star-cutsets N Sonnerat, A Vetta Electronic Notes in Discrete Mathematics 34, 107-111, 2009 | 3 | 2009 |
Network connectivity and malicious attacks N Sonnerat, A Vetta preprint, 2007 | 3 | 2007 |
Solving mixed integer programs using neural networks S Bartunov, FAG Gil, IK von Glehn, P Lichocki, I Lobov, V Nair, ... US Patent App. 18/267,363, 2024 | | 2024 |
Learning visual concepts using neural networks A Lerchner, I Higgins, N Sonnerat, AT Pal, D Hassabis, ... US Patent 11,769,057, 2023 | | 2023 |