| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 1579 | 2023 |
| Can language models learn from explanations in context? AK Lampinen, I Dasgupta, SCY Chan, K Matthewson, MH Tessler, ... Findings of the Association for Computational Linguistics: EMNLP 2022, 537--563, 2022 | 241 | 2022 |
| Are Convolutional Neural Networks or Transformers more like human vision? S Tuli, I Dasgupta, E Grant, TL Griffiths Proceedings of the Annual Meeting of the Cognitive Science Society 43 (43), 2021 | 217 | 2021 |
| Language models show human-like content effects on reasoning I Dasgupta, AK Lampinen, SCY Chan, A Creswell, D Kumaran, ... arXiv preprint arXiv:2207.07051, 2022 | 151 | 2022 |
| Where do hypotheses come from? I Dasgupta, E Schulz, SJ Gershman Cognitive psychology 96, 1-25, 2017 | 141 | 2017 |
| Evaluating compositionality in sentence embeddings I Dasgupta, D Guo, A Stuhlmüller, SJ Gershman, ND Goodman Proceedings of the Annual Meeting of the Cognitive Science Society 40 (40), 2018 | 137 | 2018 |
| Causal reasoning from meta-reinforcement learning I Dasgupta, J Wang, S Chiappa, J Mitrovic, P Ortega, D Raposo, ... arXiv preprint arXiv:1901.08162, 2019 | 132 | 2019 |
| A theory of learning to infer. I Dasgupta, E Schulz, JB Tenenbaum, SJ Gershman Psychological review 127 (3), 412, 2020 | 89 | 2020 |
| Remembrance of inferences past: Amortization in human hypothesis generation I Dasgupta, E Schulz, ND Goodman, SJ Gershman Cognition 178, 67-81, 2018 | 77 | 2018 |
| Memory as a computational resource I Dasgupta, SJ Gershman Trends in cognitive sciences 25 (3), 240-251, 2021 | 63 | 2021 |
| Collaborating with language models for embodied reasoning I Dasgupta, C Kaeser-Chen, K Marino, A Ahuja, S Babayan, F Hill, ... NeurIPS 2022 Language and Reinforcement Learning Workshop, 2022 | 55 | 2022 |
| Tell me why! explanations support learning relational and causal structure AK Lampinen, N Roy, I Dasgupta, SCY Chan, A Tam, J Mcclelland, C Yan, ... International Conference on Machine Learning, 11868-11890, 2022 | 37 | 2022 |
| Using natural language and program abstractions to instill human inductive biases in machines S Kumar, CG Correa, I Dasgupta, R Marjieh, MY Hu, R Hawkins, ... Advances in Neural Information Processing Systems 35, 167-180, 2022 | 35 | 2022 |
| Pivot: Iterative visual prompting elicits actionable knowledge for vlms S Nasiriany, F Xia, W Yu, T Xiao, J Liang, I Dasgupta, A Xie, D Driess, ... arXiv preprint arXiv:2402.07872, 2024 | 34 | 2024 |
| Transformers generalize differently from information stored in context vs in weights SCY Chan, I Dasgupta, J Kim, D Kumaran, AK Lampinen, F Hill Memory in Artificial and Real Intelligence (MemARI) workshop NeurIPS 2022, 2022 | 34 | 2022 |
| Meta-learned models of cognition M Binz, I Dasgupta, AK Jagadish, M Botvinick, JX Wang, E Schulz Behavioral and Brain Sciences, 1-38, 2023 | 28 | 2023 |
| Meta-Learning of Structured Task Distributions in Humans and Machines S Kumar, I Dasgupta, JD Cohen, ND Daw, TL Griffiths International Conference on Learning Representations, 2021, 2020 | 27* | 2020 |
| A buried ionizable residue destabilizes the native state and the transition state in the folding of monellin N Aghera, I Dasgupta, JB Udgaonkar Biochemistry 51 (45), 9058-9066, 2012 | 25 | 2012 |
| Distilling internet-scale vision-language models into embodied agents T Sumers, K Marino, A Ahuja, R Fergus, I Dasgupta arXiv preprint arXiv:2301.12507, 2023 | 19 | 2023 |
| Passive learning of active causal strategies in agents and language models A Lampinen, S Chan, I Dasgupta, A Nam, J Wang Advances in Neural Information Processing Systems 36, 2024 | 17 | 2024 |
Eric SchulzHelmholtz Munich在 helmholtz-munich.de 的电子邮件经过验证
