| Relational inductive biases, deep learning, and graph networks PW Battaglia, JB Hamrick, V Bapst, A Sanchez-Gonzalez, V Zambaldi, ... arXiv preprint arXiv:1806.01261, 2018 | 3805 | 2018 |
| Search for high-mass dilepton resonances in collisions at with the ATLAS detector G Aad, B Abbott, J Abdallah, S Abdel Khalek, O Abdinov, R Aben, B Abi, ... Physical Review D 90 (5), 052005, 2014 | 788 | 2014 |
| End-to-end differentiable physics for learning and control F de Avila Belbute-Peres, K Smith, K Allen, J Tenenbaum, JZ Kolter Advances in neural information processing systems 31, 2018 | 445 | 2018 |
| Differentiable physics and stable modes for tool-use and manipulation planning MA Toussaint, KR Allen, KA Smith, JB Tenenbaum Robotics: Science and systems foundation, 2018 | 326 | 2018 |
| Infinite mixture prototypes for few-shot learning K Allen, E Shelhamer, H Shin, J Tenenbaum International conference on machine learning, 232-241, 2019 | 301 | 2019 |
| Relational inductive biases, deep learning, and graph networks. arXiv 2018 PW Battaglia, JB Hamrick, V Bapst, A Sanchez-Gonzalez, V Zambaldi, ... arXiv preprint arXiv:1806.01261, 2018 | 213 | 2018 |
| Residual policy learning T Silver, K Allen, J Tenenbaum, L Kaelbling arXiv preprint arXiv:1812.06298, 2018 | 193 | 2018 |
| Relational inductive bias for physical construction in humans and machines JB Hamrick, KR Allen, V Bapst, T Zhu, KR McKee, JB Tenenbaum, ... arXiv preprint arXiv:1806.01203, 2018 | 135 | 2018 |
| Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning KR Allen, KA Smith, JB Tenenbaum Proceedings of the National Academy of Sciences 117 (47), 29302-29310, 2020 | 128 | 2020 |
| Few-shot bayesian imitation learning with logical program policies T Silver, KR Allen, AK Lew, LP Kaelbling, J Tenenbaum Proceedings of the AAAI Conference on Artificial Intelligence 34 (06), 10251 …, 2020 | 49 | 2020 |
| Detecting disagreement in conversations using pseudo-monologic rhetorical structure K Allen, G Carenini, R Ng Proceedings of the 2014 Conference on Empirical Methods in Natural Language …, 2014 | 49 | 2014 |
| Physical design using differentiable learned simulators KR Allen, T Lopez-Guevara, K Stachenfeld, A Sanchez-Gonzalez, ... arXiv preprint arXiv:2202.00728, 2022 | 42 | 2022 |
| Graph network simulators can learn discontinuous, rigid contact dynamics KR Allen, TL Guevara, Y Rubanova, K Stachenfeld, A Sanchez-Gonzalez, ... Conference on Robot Learning, 1157-1167, 2023 | 34 | 2023 |
| Interactions increase forager availability and activity in harvester ants E Pless, J Queirolo, N Pinter-Wollman, S Crow, K Allen, MB Mathur, ... PloS one 10 (11), e0141971, 2015 | 32 | 2015 |
| Learning rigid dynamics with face interaction graph networks KR Allen, Y Rubanova, T Lopez-Guevara, W Whitney, ... arXiv preprint arXiv:2212.03574, 2022 | 26 | 2022 |
| The tools challenge: Rapid trial-and-error learning in physical problem solving KR Allen, KA Smith, JB Tenenbaum arXiv preprint arXiv:1907.09620 14, 2019 | 25 | 2019 |
| Learning constraint-based planning models from demonstrations J Loula, K Allen, T Silver, J Tenenbaum 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2020 | 17 | 2020 |
| Using games to understand the mind K Allen, F Brändle, M Botvinick, JE Fan, SJ Gershman, A Gopnik, ... Nature Human Behaviour, 1-9, 2024 | 16 | 2024 |
| Inverse design for fluid-structure interactions using graph network simulators K Allen, T Lopez-Guevara, KL Stachenfeld, A Sanchez Gonzalez, ... Advances in Neural Information Processing Systems 35, 13759-13774, 2022 | 13 | 2022 |
| Discovering a symbolic planning language from continuous experience. J Loula, T Silver, KR Allen, J Tenenbaum CogSci, 2193, 2019 | 10 | 2019 |
Joshua B. TenenbaumMITVerifierad e-postadress på mit.edu
