| Brain-Score: Which artificial neural network for object recognition is most brain-like? M Schrimpf*, J Kubilius*, H Hong, NJ Majaj, R Rajalingham, EB Issa, ... bioRxiv, 2018 | 536 | 2018 |
| The neural architecture of language: Integrative modeling converges on predictive processing M Schrimpf, IA Blank, G Tuckute, C Kauf, EA Hosseini, NG Kanwisher, ... Proceedings of the National Academy of Sciences (PNAS) 118 (45), 2021 | 496* | 2021 |
| Unsupervised neural network models of the ventral visual stream C Zhuang, S Yan, A Nayebi, M Schrimpf, MC Frank, JJ DiCarlo, ... Proceedings of the National Academy of Sciences (PNAS) 118 (3), 2021 | 344 | 2021 |
| Threedworld: A platform for interactive multi-modal physical simulation C Gan, J Schwartz, S Alter, M Schrimpf, J Traer, J De Freitas, J Kubilius, ... Neural Information Processing Systems (NeurIPS Oral) Datasets and Benchmarks …, 2021 | 268 | 2021 |
| Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs J Kubilius*, M Schrimpf*, K Kar, R Rajalingham, H Hong, N Majaj, E Issa, ... Advances in Neural Information Processing Systems (NeurIPS Oral), 12785-12796, 2019 | 265 | 2019 |
| Recurrent computations for visual pattern completion H Tang*, M Schrimpf*, W Lotter*, C Moerman, A Paredes, JO Caro, ... Proceedings of the National Academy of Sciences (PNAS) 115 (35), 8835-8840, 2018 | 223 | 2018 |
| Integrative benchmarking to advance neurally mechanistic models of human intelligence M Schrimpf, J Kubilius, MJ Lee, NAR Murty, R Ajemian, JJ DiCarlo Neuron 108 (3), 413-423, 2020 | 213 | 2020 |
| Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations J Dapello*, T Marques*, M Schrimpf, F Geiger, DD Cox, JJ DiCarlo Neural Information Processing Systems (NeurIPS Spotlight), 2020 | 198 | 2020 |
| CORnet: Modeling the neural mechanisms of core object recognition J Kubilius*, M Schrimpf*, A Nayebi, D Bear, DLK Yamins, JJ DiCarlo bioRxiv, 2018 | 166* | 2018 |
| On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations N Cheney*, M Schrimpf*, G Kreiman CBMM Memo, 2017 | 46 | 2017 |
| Artificial neural network language models predict human brain responses to language even after a developmentally realistic amount of training EA Hosseini, M Schrimpf, Y Zhang, S Bowman, N Zaslavsky, E Fedorenko Neurobiology of Language 5 (1), 43-63, 2024 | 41* | 2024 |
| Driving and suppressing the human language network using large language models G Tuckute, A Sathe, S Srikant, M Taliaferro, M Wang, M Schrimpf, K Kay, ... Nature Human Behaviour 8 (3), 544-561, 2024 | 38 | 2024 |
| Beyond linear regression: mapping models in cognitive neuroscience should align with research goals AA Ivanova, M Schrimpf, S Anzellotti, N Zaslavsky, E Fedorenko, L Isik Neurons, Behavior, Data Analysis, and Theory, 2022 | 32* | 2022 |
| Multi-scale hierarchical neural network models that bridge from single neurons in the primate primary visual cortex to object recognition behavior T Marques, M Schrimpf, JJ DiCarlo bioRxiv, 2021.03. 01.433495, 2021 | 29 | 2021 |
| Frivolous Units: Wider Networks Are Not Really That Wide S Casper, X Boix, V D'Amario, L Guo, M Schrimpf, K Vinken, G Kreiman Proceedings of the AAAI Conference on Artificial Intelligence, 6921-6929, 2021 | 28* | 2021 |
| Aligning model and macaque inferior temporal cortex representations improves model-to-human behavioral alignment and adversarial robustness J Dapello*, K Kar*, M Schrimpf, R Geary, M Ferguson, DD Cox, J DiCarlo International Conference on Learning Representations (ICLR Notable Top-5%), 2023 | 23 | 2023 |
| A Flexible Approach to Automated RNN Architecture Generation M Schrimpf*, S Merity*, J Bradbury, R Socher International Conference on Learning Representations (ICLR), 2017 | 22 | 2017 |
| Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream F Geiger*, M Schrimpf*, T Marques, J DiCarlo International Conference on Learning Representations (ICLR Spotlight), 2022 | 13 | 2022 |
| Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results L Arend, Y Han, M Schrimpf, P Bashivan, K Kar, T Poggio, JJ DiCarlo, ... Center for Brains, Minds and Machines (CBMM), 2018 | 12 | 2018 |
| Continual Learning with Self-Organizing Maps P Bashivan, M Schrimpf, R Ajemian, I Rish, M Riemer, Y Tu Neural Information Processing Systems (NeurIPS) Continual Learning Workshop, 2018 | 10 | 2018 |
James J DiCarloMassachusetts Institute of Technology在 mit.edu 的电子邮件经过验证
