Author of the publication

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

Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent.

J. Lee, L. Xiao, S. Schoenholz, Y. Bahri, R. Novak, J. Sohl-Dickstein, and J. Pennington. NeurIPS, page 8570-8581. (2019)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Yasaman Rahbar

Nesrine Bahri

Daoud Bahri

Yasaman Karami

Bahri Yilmaz

Other publications of authors with the same name

Quantum Many-Body Physics Calculations with Large Language Models.H. Pan, N. Mudur, W. Taranto, M. Tikhanovskaya, S. Venugopalan, Y. Bahri, M. Brenner, and E. Kim. CoRR, (2024)The large learning rate phase of deep learning: the catapult mechanismA. Lewkowycz, Y. Bahri, E. Dyer, J. Sohl-Dickstein, and G. Gur-Ari. arXiv preprint arXiv:2003.02218, (2020)Infinite attention: NNGP and NTK for deep attention networks.J. Hron, Y. Bahri, J. Sohl-Dickstein, and R. Novak. ICML, volume 119 of Proceedings of Machine Learning Research, page 4376-4386. PMLR, (2020)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 the Imitation Game: Quantifying and extrapolating the capabilities of language models.A. Srivastava, A. Rastogi, A. Rao, A. Shoeb, A. Abid, A. Fisch, A. Brown, A. Santoro, A. Gupta, A. Garriga-Alonso and 440 other author(s). CoRR, (2022)Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes.R. Novak, L. Xiao, Y. Bahri, J. Lee, G. Yang, J. Hron, D. Abolafia, J. Pennington, and J. Sohl-Dickstein. ICLR (Poster), OpenReview.net, (2019)The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning.A. Andreassen, Y. Bahri, B. Neyshabur, and R. Roelofs. CoRR, (2021)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)

BibSonomy

Disambiguation of "Bahri, Yasaman"

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

Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent.

Please choose a person to relate this publication to

Yasaman Rahbar

Nesrine Bahri

Daoud Bahri

Yasaman Karami

Bahri Yilmaz

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Bahri, Yasaman"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent.

Please choose a person to relate this publication to

Yasaman Rahbar

Nesrine Bahri

Daoud Bahri

Yasaman Karami

Bahri Yilmaz

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
Harvard
MSOffice XML

Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent.