| On the opportunities and risks of foundation models R Bommasani arXiv preprint arXiv:2108.07258, 2021 | 8178 | 2021 |
| How to grow a mind: Statistics, structure, and abstraction JB Tenenbaum, C Kemp, TL Griffiths, ND Goodman science 331 (6022), 1279-1285, 2011 | 2506 | 2011 |
| Pyro: Deep universal probabilistic programming E Bingham, JP Chen, M Jankowiak, F Obermeyer, N Pradhan, ... Journal of Machine Learning Research, 2019 | 1818 | 2019 |
| Predicting pragmatic reasoning in language games MC Frank, ND Goodman Science 336 (6084), 998-998, 2012 | 1362 | 2012 |
| Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities G Comanici, E Bieber, M Schaekermann, I Pasupat, N Sachdeva, I Dhillon, ... arXiv preprint arXiv:2507.06261, 2025 | 1337 | 2025 |
| Star: Bootstrapping reasoning with reasoning E Zelikman, Y Wu, J Mu, N Goodman Advances in Neural Information Processing Systems 35, 15476-15488, 2022 | 1177 | 2022 |
| The double-edged sword of pedagogy: Instruction limits spontaneous exploration and discovery E Bonawitz, P Shafto, H Gweon, ND Goodman, E Spelke, L Schulz Cognition 120 (3), 322-330, 2011 | 1122 | 2011 |
| Church: a language for generative models N Goodman, V Mansinghka, D Roy, K Bonawitz, J Tenenbaum Uncertainty in Artificial Intelligence, 2008 | 1045* | 2008 |
| Pragmatic language interpretation as probabilistic inference ND Goodman, MC Frank Trends in cognitive sciences 20 (11), 818-829, 2016 | 934 | 2016 |
| One and done? Optimal decisions from very few samples E Vul, N Goodman, TL Griffiths, JB Tenenbaum Cognitive science 38 (4), 599-637, 2014 | 732 | 2014 |
| Rational use of cognitive resources: Levels of analysis between the computational and the algorithmic TL Griffiths, F Lieder, ND Goodman Topics in cognitive science 7 (2), 217-229, 2015 | 669 | 2015 |
| A Rational Analysis of Rule‐Based Concept Learning ND Goodman, JB Tenenbaum, J Feldman, TL Griffiths Cognitive Science 32 (1), 108-154, 2008 | 586 | 2008 |
| Knowledge and implicature: Modeling language understanding as social cognition ND Goodman, A Stuhlmüller Topics in cognitive science 5 (1), 173-184, 2013 | 585 | 2013 |
| Multimodal generative models for scalable weakly-supervised learning M Wu, N Goodman Advances in neural information processing systems 31, 2018 | 568 | 2018 |
| Using speakers' referential intentions to model early cross-situational word learning MC Frank, ND Goodman, JB Tenenbaum Psychological science 20 (5), 578-585, 2009 | 495 | 2009 |
| Amortized inference in probabilistic reasoning S Gershman, N Goodman Proceedings of the annual meeting of the cognitive science society 36 (36), 2014 | 485 | 2014 |
| Where science starts: Spontaneous experiments in preschoolers’ exploratory play C Cook, ND Goodman, LE Schulz Cognition 120 (3), 341-349, 2011 | 485 | 2011 |
| Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments T Xie, D Zhang, J Chen, X Li, S Zhao, R Cao, TJ Hua, Z Cheng, D Shin, ... Advances in Neural Information Processing Systems 37, 52040-52094, 2024 | 468 | 2024 |
| Learning disentangled representations with semi-supervised deep generative models B Paige, JW Van De Meent, A Desmaison, N Goodman, P Kohli, F Wood, ... Advances in neural information processing systems 30, 2017 | 448 | 2017 |
| A rational account of pedagogical reasoning: Teaching by, and learning from, examples P Shafto, ND Goodman, TL Griffiths Cognitive psychology 71, 55-89, 2014 | 437 | 2014 |
Joshua B. TenenbaumMITVerified email at mit.edu
