Jascha Sohl-Dickstein
Jascha Sohl-Dickstein
Google Brain
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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
14472017
Unrolled generative adversarial networks
L Metz, B Poole, D Pfau, J Sohl-Dickstein
International Conference on Learning Representations, 2017
7932017
Deep knowledge tracing
C Piech, J Spencer, J Huang, S Ganguli, M Sahami, L Guibas, ...
Neural Information Processing Systems, 2015
5882015
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
5332005
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
5072017
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
4922017
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
3902019
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
3642016
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
3272003
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
3202017
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
2482018
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
239*2018
Deep information propagation
SS Schoenholz, J Gilmer, S Ganguli, J Sohl-Dickstein
International Conference on Learning Representations, 2017
2222017
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
2142017
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
1872019
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
1752018
Deep unsupervised learning using nonequilibrium thermodynamics
J Sohl-Dickstein, EA Weiss, N Maheswaranathan, S Ganguli
International Conference on Machine Learning, 2015
1712015
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
1672004
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
1652019
Capacity and trainability in recurrent neural networks
J Collins, J Sohl-Dickstein, D Sussillo
International Conference on Learning Representations, 2017
1602017
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