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Jascha Sohl-Dickstein
Jascha Sohl-Dickstein
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Title
Cited by
Cited by
Year
Density estimation using Real NVP
L Dinh, J Sohl-Dickstein, S Bengio
International Conference on Learning Representations, 2017
19992017
Unrolled generative adversarial networks
L Metz, B Poole, D Pfau, J Sohl-Dickstein
International Conference on Learning Representations, 2017
8902017
Deep knowledge tracing
C Piech, J Spencer, J Huang, S Ganguli, M Sahami, L Guibas, ...
Neural Information Processing Systems, 2015
7772015
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
6982017
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
6232017
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
5532019
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
5532005
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
4312016
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
3982017
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
3382003
Deep unsupervised learning using nonequilibrium thermodynamics
J Sohl-Dickstein, EA Weiss, N Maheswaranathan, S Ganguli
International Conference on Machine Learning, 2015
3192015
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
3062018
Deep information propagation
SS Schoenholz, J Gilmer, S Ganguli, J Sohl-Dickstein
International Conference on Learning Representations, 2017
2662017
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
2652017
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
2592018
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
2502019
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
2382021
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
2352018
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
2202019
Learned optimizers that scale and generalize
O Wichrowska, N Maheswaranathan, MW Hoffman, SG Colmenarejo, ...
International Conference on Machine Learning, 2017
1942017
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