| Statistical mechanics of deep linear neural networks: The backpropagating kernel renormalization Q Li, H Sompolinsky Physical Review X 11 (3), 031059, 2021 | 104* | 2021 |
| Optimal compressed sensing strategies for an array of nonlinear olfactory receptor neurons with and without spontaneous activity S Qin, Q Li, C Tang, Y Tu Proceedings of the National Academy of Sciences 116 (41), 20286-20295, 2019 | 19* | 2019 |
| 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 | 11 | 2024 |
| Globally gated deep linear networks Q Li, H Sompolinsky Advances in Neural Information Processing Systems 35, 34789-34801, 2022 | 11 | 2022 |
| Connecting ntk and nngp: A unified theoretical framework for neural network learning dynamics in the kernel regime Y Avidan, Q Li, H Sompolinsky arXiv preprint arXiv:2309.04522, 2023 | 9 | 2023 |
| Minimax Dynamics of Optimally Balanced Spiking Networks of Excitatory and Inhibitory Neurons Q Li, C Pehlevan Advances in Neural Information Processing Systems 33, 4894-4904, 2020 | 8 | 2020 |
| Short-Term Plasticity Regulates Both Divisive Normalization and Adaptive Responses in Drosophila Olfactory System Y Liu, Q Li, C Tang, S Qin, Y Tu Frontiers in Computational Neuroscience, 92, 2021 | 3 | 2021 |
| Order parameters and phase transitions of continual learning in deep neural networks H Shan, Q Li, H Sompolinsky arXiv preprint arXiv:2407.10315, 2024 | 2 | 2024 |
| Statistical properties of the optimal sensitivity matrix for compressed sensing with nonlinear sensors S Qin, Q Li, C Tang, Y Tu APS March Meeting Abstracts 2019, R67. 006, 2019 | 1 | 2019 |
| Toward Statistical Mechanics of Deep Learning H Sompolinsky, Q Li APS March Meeting Abstracts 2022, F09. 001, 2022 | | 2022 |