| Human uncertainty makes classification more robust JC Peterson, RM Battleday, TL Griffiths, O Russakovsky Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 273 | 2019 |
| Using large-scale experiments and machine learning to discover theories of human decision-making JC Peterson, DD Bourgin, M Agrawal, D Reichman, TL Griffiths Science 372 (6547), 1209-1214, 2021 | 196 | 2021 |
| Evaluating (and Improving) the Correspondence Between Deep Neural Networks and Human Representations JC Peterson, JT Abbott, TL Griffiths Cognitive Science 42 (8), 2648-2669, 2018 | 162 | 2018 |
| What makes an object memorable? R Dubey, J Peterson, A Khosla, MH Yang, B Ghanem Proceedings of the ieee international conference on computer vision, 1089-1097, 2015 | 137 | 2015 |
| Capturing human categorization of natural images by combining deep networks and cognitive models RM Battleday, JC Peterson, TL Griffiths Nature communications 11 (1), 5418, 2020 | 112 | 2020 |
| Cognitive model priors for predicting human decisions J Peterson, D Bourgin, D Reichman, S Russell, T Griffiths International Conference on Machine Learning, 5133-5141, 2019 | 109* | 2019 |
| Adapting deep network features to capture psychological representations JC Peterson, JT Abbott, TL Griffiths arXiv preprint arXiv:1608.02164, 2016 | 92 | 2016 |
| Predicting human decisions with behavioral theories and machine learning O Plonsky, R Apel, E Ert, M Tennenholtz, D Bourgin, JC Peterson, ... arXiv preprint arXiv:1904.06866, 2019 | 77 | 2019 |
| Evaluating vector-space models of analogy D Chen, JC Peterson, TL Griffiths arXiv preprint arXiv:1705.04416, 2017 | 77 | 2017 |
| Deep neural networks and how they apply to sequential education data S Tang, JC Peterson, ZA Pardos Proceedings of the third (2016) acm conference on learning@ scale, 321-324, 2016 | 66 | 2016 |
| Deep models of superficial face judgments JC Peterson, S Uddenberg, TL Griffiths, A Todorov, JW Suchow Proceedings of the National Academy of Sciences 119 (17), e2115228119, 2022 | 55 | 2022 |
| Scaling up psychology via scientific regret minimization M Agrawal, JC Peterson, TL Griffiths Proceedings of the National Academy of Sciences 117 (16), 8825-8835, 2020 | 50 | 2020 |
| Parallelograms revisited: Exploring the limitations of vector space models for simple analogies JC Peterson, D Chen, TL Griffiths Cognition 205, 104440, 2020 | 33 | 2020 |
| Modelling student behavior using granular large scale action data from a MOOC S Tang, JC Peterson, ZA Pardos arXiv preprint arXiv:1608.04789, 2016 | 32 | 2016 |
| From convolutional neural networks to models of higherlevel cognition (and back again) RM Battleday, JC Peterson, TL Griffiths Annals of the New York Academy of Sciences 1505 (1), 55-78, 2021 | 30 | 2021 |
| Extracting lowdimensional psychological representations from convolutional neural networks A Jha, JC Peterson, TL Griffiths Cognitive science 47 (1), e13226, 2023 | 26 | 2023 |
| End-to-end deep prototype and exemplar models for predicting human behavior P Singh, JC Peterson, RM Battleday, TL Griffiths arXiv preprint arXiv:2007.08723, 2020 | 24 | 2020 |
| Modeling human categorization of natural images using deep feature representations RM Battleday, JC Peterson, TL Griffiths arXiv preprint arXiv:1711.04855, 2017 | 24 | 2017 |
| Understanding Student Success in Chemistry using Gaze Tracking & Pupillometry J Peterson, Z Pardos, M Rau, A Swigart, C Gerber, J McKinsey Artificial Intelligence in Education, 2015 | 22 | 2015 |
| Psychological and musical factors underlying engagement with unfamiliar music P Janata, J Peterson, C Ngan, B Keum, H Whiteside, S Ran Music Perception: An Interdisciplinary Journal 36 (2), 175-200, 2018 | 21 | 2018 |
Thomas L. GriffithsProfessor of Psychology and Computer Science, Princeton University在 princeton.edu 的电子邮件经过验证
