| Deep unsupervised learning using nonequilibrium thermodynamics J Sohl-Dickstein, E Weiss, N Maheswaranathan, S Ganguli International conference on machine learning, 2256-2265, 2015 | 4544 | 2015 |
| Continual learning through synaptic intelligence F Zenke, B Poole, S Ganguli International conference on machine learning, 3987-3995, 2017 | 2629 | 2017 |
| 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 | 2061 | 2013 |
| Identifying and attacking the saddle point problem in high-dimensional non-convex optimization YN Dauphin, R Pascanu, C Gulcehre, K Cho, S Ganguli, Y Bengio Advances in neural information processing systems 27, 2014 | 1755 | 2014 |
| Deep knowledge tracing C Piech, J Bassen, J Huang, S Ganguli, M Sahami, LJ Guibas, ... Advances in neural information processing systems 28, 2015 | 1417 | 2015 |
| On the expressive power of deep neural networks M Raghu, B Poole, J Kleinberg, S Ganguli, J Sohl-Dickstein international conference on machine learning, 2847-2854, 2017 | 898 | 2017 |
| A deep learning framework for neuroscience BA Richards, TP Lillicrap, P Beaudoin, Y Bengio, R Bogacz, ... Nature neuroscience 22 (11), 1761-1770, 2019 | 832 | 2019 |
| Holistic evaluation of language models P Liang, R Bommasani, T Lee, D Tsipras, D Soylu, M Yasunaga, Y Zhang, ... arXiv preprint arXiv:2211.09110, 2022 | 717 | 2022 |
| Exponential expressivity in deep neural networks through transient chaos B Poole, S Lahiri, M Raghu, J Sohl-Dickstein, S Ganguli Advances in neural information processing systems, 3360-3368, 2016 | 622 | 2016 |
| Superspike: Supervised learning in multilayer spiking neural networks F Zenke, S Ganguli Neural computation 30 (6), 1514-1541, 2018 | 591 | 2018 |
| Pruning neural networks without any data by iteratively conserving synaptic flow H Tanaka, D Kunin, DL Yamins, S Ganguli Advances in neural information processing systems 33, 6377-6389, 2020 | 583 | 2020 |
| Cortical layer–specific critical dynamics triggering perception JH Marshel, YS Kim, TA Machado, S Quirin, B Benson, J Kadmon, C Raja, ... Science 365 (6453), eaaw5202, 2019 | 528 | 2019 |
| Deep information propagation SS Schoenholz, J Gilmer, S Ganguli, J Sohl-Dickstein arXiv preprint arXiv:1611.01232, 2016 | 390 | 2016 |
| Memory traces in dynamical systems S Ganguli, D Huh, H Sompolinsky Proceedings of the national academy of sciences 105 (48), 18970-18975, 2008 | 376 | 2008 |
| On simplicity and complexity in the brave new world of large-scale neuroscience P Gao, S Ganguli Current opinion in neurobiology 32, 148-155, 2015 | 356 | 2015 |
| Compressed sensing, sparsity, and dimensionality in neuronal information processing and data analysis S Ganguli, H Sompolinsky Annual review of neuroscience 35, 485-508, 2012 | 299 | 2012 |
| Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice J Pennington, S Schoenholz, S Ganguli Advances in neural information processing systems 30, 2017 | 292 | 2017 |
| Deep learning on a data diet: Finding important examples early in training M Paul, S Ganguli, GK Dziugaite Advances in Neural Information Processing Systems 34, 20596-20607, 2021 | 289 | 2021 |
| Deep learning models of the retinal response to natural scenes L McIntosh, N Maheswaranathan, A Nayebi, S Ganguli, S Baccus Advances in neural information processing systems, 1369-1377, 2016 | 284 | 2016 |
| Understanding self-supervised learning dynamics without contrastive pairs Y Tian, X Chen, S Ganguli International Conference on Machine Learning, 10268-10278, 2021 | 281 | 2021 |
Niru MaheswaranathanMeta Reality Labs在 fb.com 的电子邮件经过验证
