| A global geometric framework for nonlinear dimensionality reduction JB Tenenbaum, V Silva, JC Langford science 290 (5500), 2319-2323, 2000 | 16530 | 2000 |
| Human-level concept learning through probabilistic program induction BM Lake, R Salakhutdinov, JB Tenenbaum Science 350 (6266), 1332-1338, 2015 | 3231 | 2015 |
| Building machines that learn and think like people BM Lake, TD Ullman, JB Tenenbaum, SJ Gershman Behavioral and brain sciences 40, e253, 2017 | 2761 | 2017 |
| Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling J Wu, C Zhang, T Xue, B Freeman, J Tenenbaum Advances in neural information processing systems 29, 2016 | 2031 | 2016 |
| How to grow a mind: Statistics, structure, and abstraction JB Tenenbaum, C Kemp, TL Griffiths, ND Goodman Science 331 (6022), 1279-1285, 2011 | 1973 | 2011 |
| The large‐scale structure of semantic networks: Statistical analyses and a model of semantic growth M Steyvers, JB Tenenbaum Cognitive science 29 (1), 41-78, 2005 | 1775 | 2005 |
| Hierarchical topic models and the nested Chinese restaurant process T Griffiths, M Jordan, J Tenenbaum, D Blei Advances in neural information processing systems 16, 2003 | 1493 | 2003 |
| Topics in semantic representation. TL Griffiths, M Steyvers, JB Tenenbaum Psychological review 114 (2), 211, 2007 | 1404 | 2007 |
| Meta-learning for semi-supervised few-shot classification M Ren, E Triantafillou, S Ravi, J Snell, K Swersky, JB Tenenbaum, ... arXiv preprint arXiv:1803.00676, 2018 | 1363 | 2018 |
| Word learning as Bayesian inference. F Xu, JB Tenenbaum Psychological review 114 (2), 245, 2007 | 1292 | 2007 |
| Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation TD Kulkarni, K Narasimhan, A Saeedi, J Tenenbaum Advances in neural information processing systems 29, 2016 | 1274 | 2016 |
| Global versus local methods in nonlinear dimensionality reduction V Silva, J Tenenbaum Advances in neural information processing systems 15, 2002 | 1242 | 2002 |
| Theory-based Bayesian models of inductive learning and reasoning JB Tenenbaum, TL Griffiths, C Kemp Trends in cognitive sciences 10 (7), 309-318, 2006 | 1101 | 2006 |
| Deep convolutional inverse graphics network TD Kulkarni, WF Whitney, P Kohli, J Tenenbaum Advances in neural information processing systems 28, 2015 | 1072 | 2015 |
| Causal inference in multisensory perception KP Körding, U Beierholm, WJ Ma, S Quartz, JB Tenenbaum, L Shams PLoS one 2 (9), e943, 2007 | 1062 | 2007 |
| Separating style and content with bilinear models JB Tenenbaum, WT Freeman Neural Computation 12 (6), 1247-1283, 2000 | 1048 | 2000 |
| Action understanding as inverse planning CL Baker, R Saxe, JB Tenenbaum Cognition 113 (3), 329-349, 2009 | 1038 | 2009 |
| Church: a language for generative models N Goodman, V Mansinghka, D Roy, K Bonawitz, JB Tenenbaum arXiv preprint arXiv:1206.3255, 2012 | 955 | 2012 |
| One shot learning of simple visual concepts B Lake, R Salakhutdinov, J Gross, J Tenenbaum Proceedings of the annual meeting of the cognitive science society 33 (33), 2011 | 948 | 2011 |
| Generalization, similarity, and Bayesian inference JB Tenenbaum, TL Griffiths Behavioral and brain sciences 24 (4), 629-640, 2001 | 931 | 2001 |
Thomas L. GriffithsProfessor of Psychology and Computer Science, Princeton University在 princeton.edu 的電子郵件地址已通過驗證
