| Deep unsupervised learning using nonequilibrium thermodynamics J Sohl-Dickstein, EA Weiss, N Maheswaranathan, S Ganguli International Conference on Machine Learning, 2015 | 5583 | 2015 |
| Score-Based Generative Modeling through Stochastic Differential Equations Y Song, J Sohl-Dickstein, DP Kingma, A Kumar, S Ermon, B Poole ICLR, oral, outstanding paper award, 2021 | 4635 | 2021 |
| Density estimation using Real NVP L Dinh, J Sohl-Dickstein, S Bengio International Conference on Learning Representations, 2017 | 4037 | 2017 |
| Deep knowledge tracing C Piech, J Spencer, J Huang, S Ganguli, M Sahami, L Guibas, ... Neural Information Processing Systems, 2015 | 1564 | 2015 |
| Unrolled generative adversarial networks L Metz, B Poole, D Pfau, J Sohl-Dickstein International Conference on Learning Representations, 2017 | 1334 | 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 | 1286 | 2017 |
| 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, ... Neural Information Processing Systems, 2019 | 1101 | 2019 |
| Beyond the imitation game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... TMLR, 2022 | 1024 | 2022 |
| 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 | 974 | 2017 |
| Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability M Raghu, J Gilmer, J Yosinski, J Sohl-Dickstein Neural Information Processing Systems, 2017 | 708 | 2017 |
| Exponential expressivity in deep neural networks through transient chaos B Poole, S Lahiri, M Raghu, J Sohl-Dickstein, S Ganguli Neural Information Processing Systems, 3360-3368, 2016 | 663 | 2016 |
| 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 | 617 | 2005 |
| 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 | 493 | 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 | 445 | 2019 |
| Deep information propagation SS Schoenholz, J Gilmer, S Ganguli, J Sohl-Dickstein International Conference on Learning Representations, 2017 | 434 | 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 | 382 | 2018 |
| Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes R Novak, L Xiao, J Lee, Y Bahri, G Yang, D Abolafia, J Pennington, ... International Conference on Learning Representations, 2019 | 379 | 2019 |
| 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 | 375 | 2018 |
| 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 | 358 | 2003 |
| Rebar: Low-variance, unbiased gradient estimates for discrete latent variable models G Tucker, A Mnih, CJ Maddison, J Lawson, J Sohl-Dickstein Neural Information Processing Systems, oral presentation, 2627-2636, 2017 | 354 | 2017 |
Niru MaheswaranathanMeta Reality LabsVerified email at fb.com
