| Cognitive model discovery via disentangled rnns K Miller, M Eckstein, M Botvinick, Z Kurth-Nelson Advances in Neural Information Processing Systems 36, 2024 | 5 | 2024 |
| Meta-in-context learning in large language models J Coda-Forno, M Binz, Z Akata, M Botvinick, J Wang, E Schulz Advances in Neural Information Processing Systems 36, 2024 | 13 | 2024 |
| Neural networks implementing attention over object embeddings for object-centric visual reasoning F Ding, AA Santoro, FG Hill, M Botvinick, L Piloto US Patent App. 18/029,980, 2024 | | 2024 |
| Whole-body simulation of realistic fruit fly locomotion with deep reinforcement learning R Vaxenburg, I Siwanowicz, J Merel, AA Robie, C Morrow, G Novati, ... bioRxiv, 2024.03. 11.584515, 2024 | | 2024 |
| How should the advent of large language models affect the practice of science? M Binz, S Alaniz, A Roskies, B Aczel, CT Bergstrom, C Allen, D Schad, ... arXiv preprint arXiv:2312.03759, 2023 | 4 | 2023 |
| Meta-learned models of cognition M Binz, I Dasgupta, AK Jagadish, M Botvinick, JX Wang, E Schulz Behavioral and Brain Sciences, 1-38, 2023 | 16 | 2023 |
| Pretrial predictors of conflict response efficacy in the human prefrontal cortex AB Herman, EH Smith, CA Schevon, MJ Yates, GM McKhann, M Botvinick, ... Iscience 26 (11), 2023 | 1 | 2023 |
| Generative replay underlies compositional inference in the hippocampal-prefrontal circuit P Schwartenbeck, A Baram, Y Liu, S Mark, T Muller, R Dolan, M Botvinick, ... Cell 186 (22), 4885-4897. e14, 2023 | 6 | 2023 |
| Scaffolding cooperation in human groups with deep reinforcement learning KR McKee, A Tacchetti, MA Bakker, J Balaguer, L Campbell-Gillingham, ... Nature Human Behaviour 7 (10), 1787-1796, 2023 | 5 | 2023 |
| Learning visual concepts using neural networks A Lerchner, I Higgins, N Sonnerat, AT Pal, D Hassabis, ... US Patent 11,769,057, 2023 | | 2023 |
| Generating sequences of data elements using cross-attention operations CGM Hawthorne, AC Jaegle, CC Cangea, SBD Avocat, CTC Nash, ... US Patent App. 18/102,985, 2023 | | 2023 |
| Meta-in-context learning in large language models Z Akata, M Binz, J Coda-Forno, M Botvinick, JX Wang, E Schulz arXiv, 2023 | | 2023 |
| Discogen: Learning to discover gene regulatory networks NR Ke, SJ Dunn, J Bornschein, S Chiappa, M Rey, JB Lespiau, ... arXiv preprint arXiv:2304.05823, 2023 | 3 | 2023 |
| Catalyzing next-generation artificial intelligence through neuroai A Zador, S Escola, B Richards, B Ölveczky, Y Bengio, K Boahen, ... Nature communications 14 (1), 1597, 2023 | 95 | 2023 |
| Using games to understand the mind KR Allen, F Brändle, M Botvinick, J Fan, SJ Gershman, TL Griffiths, ... PsyArXiv, 2023 | 8 | 2023 |
| Compositional sequence generation in the entorhinal–hippocampal system DC McNamee, KL Stachenfeld, MM Botvinick, SJ Gershman Entropy 24 (12), 1791, 2022 | 4 | 2022 |
| BCI learning phenomena can be explained by gradient-based optimization PC Humphreys, K Daie, K Svoboda, M Botvinick, TP Lillicrap bioRxiv, 2022.12. 08.519453, 2022 | 4 | 2022 |
| Fine-tuning language models to find agreement among humans with diverse preferences M Bakker, M Chadwick, H Sheahan, M Tessler, L Campbell-Gillingham, ... Advances in Neural Information Processing Systems 35, 38176-38189, 2022 | 96 | 2022 |
| Realizing the promise of AI: a new calling for cognitive science MM Botvinick Trends in Cognitive Sciences 26 (12), 1013-1014, 2022 | 6 | 2022 |
| A unified theory of dual-process control T Moskovitz, K Miller, M Sahani, MM Botvinick arXiv preprint arXiv:2211.07036, 2022 | 4 | 2022 |
