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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, S. Schoenholz, and J. Pennington. ICML, volume 80 of Proceedings of Machine Learning Research, page 5389-5398. PMLR, (2018)

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Ludwig Schoenholz

S Danischewski

S Hüttelmaier

S Leopold

S Steinlechner

Other publications of authors with the same name

Explaining the Learning Dynamics of Direct Feedback Alignment.J. Gilmer, C. Raffel, S. Schoenholz, M. Raghu, and J. Sohl-Dickstein. ICLR (Workshop), OpenReview.net, (2017)Fast Finite Width Neural Tangent Kernel.R. Novak, J. Sohl-Dickstein, and S. Schoenholz. ICML, volume 162 of Proceedings of Machine Learning Research, page 17018-17044. PMLR, (2022)Specialization as an optimal strategy under varying external conditions.M. Hsieh, Á. Halász, E. Cubuk, S. Schoenholz, and A. Martinoli. ICRA, page 1941-1946. IEEE, (2009)Finite Versus Infinite Neural Networks: an Empirical Study.J. Lee, S. Schoenholz, J. Pennington, B. Adlam, L. Xiao, R. Novak, and J. Sohl-Dickstein. NeurIPS, (2020)Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks.M. Chen, J. Pennington, and S. Schoenholz. ICML, volume 80 of Proceedings of Machine Learning Research, page 872-881. PMLR, (2018)The emergence of spectral universality in deep networks.J. Pennington, S. Schoenholz, and S. Ganguli. AISTATS, volume 84 of Proceedings of Machine Learning Research, page 1924-1932. PMLR, (2018)Neural Tangents: Fast and Easy Infinite Neural Networks in Python.R. Novak, L. Xiao, J. Hron, J. Lee, A. Alemi, J. Sohl-Dickstein, and S. Schoenholz. CoRR, (2019)End-to-End Differentiable Reactive Molecular Dynamics Simulations Using JAX.M. Kaymak, S. Schoenholz, E. Cubuk, K. O'Hearn, K. Jr., and H. Aktulga. ISC, volume 13948 of Lecture Notes in Computer Science, page 202-219. Springer, (2023)MetaInit: Initializing learning by learning to initialize.Y. Dauphin, and S. Schoenholz. NeurIPS, page 12624-12636. (2019)JAX MD: A Framework for Differentiable Physics.S. Schoenholz, and E. Cubuk. NeurIPS, (2020)

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Disambiguation of "Schoenholz, Samuel S."

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Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10, 000-Layer Vanilla Convolutional Neural Networks.

Please choose a person to relate this publication to

Ludwig Schoenholz

S Danischewski

S Hüttelmaier

S Leopold

S Steinlechner

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Schoenholz, Samuel S."

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10, 000-Layer Vanilla Convolutional Neural Networks.

Please choose a person to relate this publication to

Ludwig Schoenholz

S Danischewski

S Hüttelmaier

S Leopold

S Steinlechner

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
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MSOffice XML

Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10, 000-Layer Vanilla Convolutional Neural Networks.