| 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 | 373 | 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 | 285 | 2018 |
| Multiply-constrained semantic search in the Remote Associates Test KA Smith, DE Huber, E Vul Cognition 128, 64-75, 2013 | 139 | 2013 |
| Sources of uncertainty in intuitive physics KA Smith, E Vul Topics in cognitive science 5 (1), 185-199, 2013 | 132 | 2013 |
| 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 | 86 | 2020 |
| Modeling expectation violation in intuitive physics with coarse probabilistic object representations K Smith, L Mei, S Yao, J Wu, E Spelke, J Tenenbaum, T Ullman Advances in neural information processing systems 32, 2019 | 65 | 2019 |
| Consistent physics underlying ballistic motion prediction KA Smith, P Battaglia, E Vul Annual Meeting of the Cognitive Science Society, 2013 | 50 | 2013 |
| Think again? The amount of mental simulation tracks uncertainty in the outcome. JB Hamrick, KA Smith, TL Griffiths, E Vul CogSci, 2015 | 49 | 2015 |
| Agent: A benchmark for core psychological reasoning T Shu, A Bhandwaldar, C Gan, K Smith, S Liu, D Gutfreund, E Spelke, ... International Conference on Machine Learning, 9614-9625, 2021 | 47 | 2021 |
| Physical predictions over time KA Smith, E Dechter, JB Tenenbaum, E Vul Annual Meeting of the Cognitive Science Society, 2013 | 38 | 2013 |
| Different physical intuitions exist between tasks, not domains KA Smith, PW Battaglia, E Vul Computational Brain & Behavior 1, 101-118, 2018 | 37 | 2018 |
| Empirical evidence for markov chain monte carlo in memory search D Bourgin, J Abbott, T Griffiths, K Smith, E Vul Proceedings of the Annual Meeting of the Cognitive Science Society 36 (36), 2014 | 36 | 2014 |
| Physion: Evaluating physical prediction from vision in humans and machines DM Bear, E Wang, D Mrowca, FJ Binder, HYF Tung, RT Pramod, ... arXiv preprint arXiv:2106.08261, 2021 | 32 | 2021 |
| Unsupervised discovery of 3d physical objects from video Y Du, K Smith, T Ulman, J Tenenbaum, J Wu arXiv preprint arXiv:2007.12348, 2020 | 28 | 2020 |
| 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 |
| The Galileo system for the electronic management of learning JR Bergan, JR Bergan, M Rattee, JK Feld, K Smith, K Cunningham, ... Assessment Technology, Inc. Retrieved online September 19, 2006, 2003 | 25 | 2003 |
| The'Fundamental Attribution Error'is rational in an uncertain world. D Walker, KA Smith, E Vul CogSci, 2015 | 22 | 2015 |
| Partial mental simulation explains fallacies in physical reasoning I Bass, KA Smith, E Bonawitz, TD Ullman Cognitive Neuropsychology 38 (7-8), 413-424, 2021 | 19 | 2021 |
| Looking forwards and backwards: Similarities and differences in prediction and retrodiction K Smith, E Vul Proceedings of the Annual Meeting of the Cognitive Science Society 36 (36), 2014 | 18 | 2014 |
| Data-efficient learning for complex and real-time physical problem solving using augmented simulation K Ota, DK Jha, D Romeres, J van Baar, KA Smith, T Semitsu, T Oiki, ... IEEE Robotics and Automation Letters 6 (2), 4241-4248, 2021 | 16 | 2021 |
Joshua B. TenenbaumMITVerified email at mit.edu
