| Exact solutions to the nonlinear dynamics of learning in deep linear neural networks AM Saxe, JL McClelland, S Ganguli arXiv preprint arXiv:1312.6120, 2013 | 2146 | 2013 |
| A deep learning framework for neuroscience BA Richards, TP Lillicrap, P Beaudoin, Y Bengio, R Bogacz, ... Nature neuroscience 22 (11), 1761-1770, 2019 | 874 | 2019 |
| On the information bottleneck theory of deep learning AM Saxe, Y Bansal, J Dapello, M Advani, A Kolchinsky, BD Tracey, ... Journal of Statistical Mechanics: Theory and Experiment 2019 (12), 124020, 2019 | 617 | 2019 |
| Qualitatively characterizing neural network optimization problems IJ Goodfellow, O Vinyals, AM Saxe arXiv preprint arXiv:1412.6544, 2014 | 611 | 2014 |
| Measuring invariances in deep networks I Goodfellow, H Lee, Q Le, A Saxe, A Ng Advances in neural information processing systems 22, 2009 | 596 | 2009 |
| On random weights and unsupervised feature learning. AM Saxe, PW Koh, Z Chen, M Bhand, B Suresh, AY Ng Icml 2 (3), 6, 2011 | 536 | 2011 |
| High-dimensional dynamics of generalization error in neural networks MS Advani, AM Saxe, H Sompolinsky Neural Networks 132, 428-446, 2020 | 496 | 2020 |
| If deep learning is the answer, what is the question? A Saxe, S Nelli, C Summerfield Nature Reviews Neuroscience 22 (1), 55-67, 2021 | 310 | 2021 |
| A mathematical theory of semantic development in deep neural networks AM Saxe, JL McClelland, S Ganguli Proceedings of the National Academy of Sciences 116 (23), 11537-11546, 2019 | 290 | 2019 |
| Acquisition of decision making criteria: reward rate ultimately beats accuracy F Balci, P Simen, R Niyogi, A Saxe, JA Hughes, P Holmes, JD Cohen Attention, Perception, & Psychophysics 73, 640-657, 2011 | 207 | 2011 |
| Orthogonal representations for robust context-dependent task performance in brains and neural networks T Flesch, K Juechems, T Dumbalska, A Saxe, C Summerfield Neuron 110 (7), 1258-1270. e11, 2022 | 171* | 2022 |
| Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup S Goldt, M Advani, AM Saxe, F Krzakala, L Zdeborová Advances in neural information processing systems 32, 2019 | 156 | 2019 |
| Deterministic matrices matching the compressed sensing phase transitions of Gaussian random matrices H Monajemi, S Jafarpour, M Gavish, Stat 330/CME 362 Collaboration, ... Proceedings of the National Academy of Sciences 110 (4), 1181-1186, 2013 | 124 | 2013 |
| Multitasking capability versus learning efficiency in neural network architectures S Musslick, A Saxe, K Özcimder, B Dey, G Henselman, JD Cohen Cognitive Science Society, 2017 | 91 | 2017 |
| Energy–entropy competition and the effectiveness of stochastic gradient descent in machine learning Y Zhang, AM Saxe, MS Advani, AA Lee Molecular Physics 116 (21-22), 3214-3223, 2018 | 76 | 2018 |
| Learning hierarchical category structure in deep neural networks AM Saxe, JL McClelland, S Ganguli Proceedings of the 35th annual meeting of the Cognitive Science Society …, 2013 | 76* | 2013 |
| Continual learning in the teacher-student setup: Impact of task similarity S Lee, S Goldt, A Saxe International Conference on Machine Learning, 6109-6119, 2021 | 64 | 2021 |
| Unsupervised learning models of primary cortical receptive fields and receptive field plasticity A Saxe, M Bhand, R Mudur, B Suresh, AY Ng Shawe-Taylor, J.; Zemel, R.; Bartlett, P, 2011 | 60 | 2011 |
| Active long term memory networks T Furlanello, J Zhao, AM Saxe, L Itti, BS Tjan arXiv preprint arXiv:1606.02355, 2016 | 54 | 2016 |
| Hierarchy through composition with multitask LMDPs AM Saxe, AC Earle, B Rosman International Conference on Machine Learning, 3017-3026, 2017 | 45 | 2017 |
James L. McClellandStanford University在 stanford.edu 的电子邮件经过验证
