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A Correspondence Between Random Neural Networks and Statistical Field Theory.

S. Schoenholz, J. Pennington, and J. Sohl-Dickstein. CoRR, (2017)

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Jeffrey Johnson

Jeffrey Gaynor

Jeffrey Donahue

jeffrey Wimmer

Jeffrey Windisch

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A Random Matrix Perspective on Mixtures of Nonlinearities in High Dimensions.B. Adlam, J. Levinson, and J. Pennington. AISTATS, volume 151 of Proceedings of Machine Learning Research, page 3434-3457. PMLR, (2022)Second-order regression models exhibit progressive sharpening to the edge of stability.A. Agarwala, F. Pedregosa, and J. Pennington. ICML, volume 202 of Proceedings of Machine Learning Research, page 169-195. PMLR, (2023)Nonlinear random matrix theory for deep learning.J. Pennington, and P. Worah. NIPS, page 2637-2646. (2017)Clinical Data Research Network Lessons Learned.K. Murphy, J. Pennington, A. Browne, B. Ruth, R. Khare, L. Davidson, P. Morris, L. Utidjian, and C. Bailey. CRI, AMIA, (2016)Deep Neural Networks as Gaussian Processes.J. Lee, Y. Bahri, R. Novak, S. Schoenholz, J. Pennington, and J. Sohl-Dickstein. ICLR (Poster), OpenReview.net, (2018)Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models.A. Singh, J. Co-Reyes, R. Agarwal, A. Anand, P. Patil, X. Garcia, P. Liu, J. Harrison, J. Lee, K. Xu and 31 other author(s). Trans. Mach. Learn. Res., (2024)Geometry of Neural Network Loss Surfaces via Random Matrix Theory.J. Pennington, and Y. Bahri. ICML, volume 70 of Proceedings of Machine Learning Research, page 2798-2806. PMLR, (2017)Bayesian Deep Convolutional Networks with Many Channels are Gaussian ProcessesR. Novak, L. Xiao, J. Lee, Y. Bahri, G. Yang, J. Hron, D. Abolafia, J. Pennington, and J. Sohl-Dickstein. (2018)cite arxiv:1810.05148Comment: Published as a conference paper at ICLR 2019.Statistical Mechanics of Deep LearningY. Bahri, J. Kadmon, J. Pennington, S. Schoenholz, J. Sohl-Dickstein, and S. Ganguli. Annual Review of Condensed Matter Physics, 11 (1): null (2020)A Mean Field Theory of Batch NormalizationG. Yang, J. Pennington, V. Rao, J. Sohl-Dickstein, and S. Schoenholz. (2019)cite arxiv:1902.08129Comment: To appear in ICLR 2019.

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Disambiguation of "Pennington, Jeffrey"

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A Correspondence Between Random Neural Networks and Statistical Field Theory.

Please choose a person to relate this publication to

Jeffrey Johnson

Jeffrey Gaynor

Jeffrey Donahue

jeffrey Wimmer

Jeffrey Windisch

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Pennington, Jeffrey"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML A Correspondence Between Random Neural Networks and Statistical Field Theory.

Please choose a person to relate this publication to

Jeffrey Johnson

Jeffrey Gaynor

Jeffrey Donahue

jeffrey Wimmer

Jeffrey Windisch

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
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URL
DOI
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APA
Chicago
DIN 1505
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A Correspondence Between Random Neural Networks and Statistical Field Theory.