| Density estimation using Real NVP L Dinh, J Sohl-Dickstein, S Bengio International Conference on Learning Representations, 2017 | 1158 | 2017 |
| Unrolled generative adversarial networks L Metz, B Poole, D Pfau, J Sohl-Dickstein International Conference on Learning Representations, 2017 | 683 | 2017 |
| Stratigraphy and sedimentology of a dry to wet eolian depositional system, Burns formation, Meridiani Planum, Mars JP Grotzinger, RE Arvidson, JF Bell Iii, W Calvin, BC Clark, DA Fike, ... Earth and Planetary Science Letters 240 (1), 11-72, 2005 | 517 | 2005 |
| Deep knowledge tracing C Piech, J Spencer, J Huang, S Ganguli, M Sahami, L Guibas, ... arXiv preprint arXiv:1506.05908, 2015 | 465 | 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, 2017 | 438 | 2017 |
| Deep neural networks as gaussian processes J Lee, Y Bahri, R Novak, SS Schoenholz, J Pennington, J Sohl-Dickstein International Conference on Learning Representations, 2017 | 400 | 2017 |
| 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 | 326 | 2016 |
| Mars exploration rover Athena panoramic camera (Pancam) investigation JF Bell III, SW Squyres, KE Herkenhoff, JN Maki, HM Arneson, D Brown, ... Journal of Geophysical Research: Planets 108 (E12), 2003 | 323 | 2003 |
| Wide neural networks of any depth evolve as linear models under gradient descent J Lee, L Xiao, SS Schoenholz, Y Bahri, R Novak, J Sohl-Dickstein, ... arXiv preprint arXiv:1902.06720, 2019 | 297 | 2019 |
| Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability M Raghu, J Gilmer, J Yosinski, J Sohl-Dickstein arXiv preprint arXiv:1706.05806, 2017 | 252 | 2017 |
| Adversarial examples that fool both computer vision and time-limited humans GF Elsayed, S Shankar, B Cheung, N Papernot, A Kurakin, I Goodfellow, ... Neural Information Processing Systems, 2018 | 218 | 2018 |
| Sensitivity and generalization in neural networks: an empirical study R Novak, Y Bahri, DA Abolafia, J Pennington, J Sohl-Dickstein International Conference on Learning Representations, 2018 | 212 | 2018 |
| Rebar: Low-variance, unbiased gradient estimates for discrete latent variable models G Tucker, A Mnih, CJ Maddison, J Lawson, J Sohl-Dickstein Advances in Neural Information Processing Systems, oral track, 2627-2636, 2017 | 195 | 2017 |
| Deep information propagation SS Schoenholz, J Gilmer, S Ganguli, J Sohl-Dickstein International Conference on Learning Representations, 2017 | 191 | 2017 |
| New method for parameter estimation in probabilistic models: minimum probability flow J Sohl-Dickstein, PB Battaglino, MR DeWeese Physical review letters 107 (22), 220601, 2011 | 168* | 2011 |
| Pancam multispectral imaging results from the Spirit rover at Gusev Crater JF Bell, SW Squyres, RE Arvidson, HM Arneson, D Bass, D Blaney, ... Science 305 (5685), 800-806, 2004 | 165 | 2004 |
| Athena microscopic imager investigation KE Herkenhoff, SW Squyres, JF Bell, JN Maki, HM Arneson, P Bertelsen, ... Journal of Geophysical Research: Planets 108 (E12), 2003 | 155 | 2003 |
| Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks L Xiao, Y Bahri, J Sohl-Dickstein, SS Schoenholz, J Pennington International Conference on Machine Learning, 2018 | 152 | 2018 |
| Measuring the effects of data parallelism on neural network training CJ Shallue, J Lee, J Antognini, J Sohl-Dickstein, R Frostig, GE Dahl Journal of Machine Learning Research, 2019 | 149 | 2019 |
| Capacity and trainability in recurrent neural networks J Collins, J Sohl-Dickstein, D Sussillo International Conference on Learning Representations, 2017 | 145 | 2017 |
Jeffrey PenningtonGoogle BrainVerified email at google.com
