| Beyond neural scaling laws: beating power law scaling via data pruning B Sorscher, R Geirhos, S Shekhar, S Ganguli, A Morcos Advances in Neural Information Processing Systems 35, 19523-19536, 2022 | 304 | 2022 |
| A unified theory for the computational and mechanistic origins of grid cells B Sorscher, GC Mel, SA Ocko, LM Giocomo, S Ganguli Neuron 111 (1), 121-137. e13, 2023 | 77 | 2023 |
| A unified theory for the origin of grid cells through the lens of pattern formation B Sorscher, G Mel, S Ganguli, SA Ocko Advances in Neural Information Processing Systems 32, 2019 | 73 | 2019 |
| Neural representational geometry underlies few-shot concept learning B Sorscher, S Ganguli, H Sompolinsky Proceedings of the National Academy of Sciences 119 (43), e2200800119, 2022 | 61 | 2022 |
| Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks A Nayebi, A Attinger, M Campbell, K Hardcastle, I Low, CS Mallory, G Mel, ... Advances in Neural Information Processing Systems 34, 12167-12179, 2021 | 31 | 2021 |
| One-shot entorhinal maps enable flexible navigation in novel environments JH Wen, B Sorscher, S Ganguli, LM Giocomo bioRxiv, 2023.09. 07.556744, 2023 | 5 | 2023 |
| When and why grid cells appear or not in trained path integrators B Sorscher, GC Mel, A Nayebi, L Giocomo, D Yamins, S Ganguli bioRxiv, 2022.11. 14.516537, 2022 | 5 | 2022 |
| Representations and generalization in artificial and brain neural networks Q Li, B Sorscher, H Sompolinsky Proceedings of the National Academy of Sciences 121 (27), e2311805121, 2024 | 2 | 2024 |
| A theory of weight distribution-constrained learning W Zhong, B Sorscher, D Lee, H Sompolinsky Advances in Neural Information Processing Systems 35, 14113-14127, 2022 | 2 | 2022 |
| A theory of learning with constrained weight-distribution W Zhong, B Sorscher, DD Lee, H Sompolinsky arXiv preprint arXiv:2206.08933, 2022 | 2 | 2022 |
Surya GanguliAssociate Professor, Stanford UniversityVerified email at stanford.edu
