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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)

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Fang Xiao

Wenjiang Xiao

Meifang Xiao

Jianping Xiao

Chun Xiao

Other publications of authors with the same name

Disentangling trainability and generalization in deep learning.L. Xiao, J. Pennington, and S. Schoenholz. CoRR, (2019)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. ICLR, OpenReview.net, (2020)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.Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5?C. Freeman, L. Culp, A. Parisi, M. Bileschi, G. Elsayed, A. Rizkowsky, I. Simpson, A. Alemi, A. Nova, B. Adlam and 20 other author(s). CoRR, (2023)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)Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent.J. Lee, L. Xiao, S. Schoenholz, Y. Bahri, J. Sohl-Dickstein, and J. Pennington. CoRR, (2019)Small-scale proxies for large-scale Transformer training instabilities.M. Wortsman, P. Liu, L. Xiao, K. Everett, A. Alemi, B. Adlam, J. Co-Reyes, I. Gur, A. Kumar, R. Novak and 6 other author(s). CoRR, (2023)The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks.W. Hu, L. Xiao, B. Adlam, and J. Pennington. NeurIPS, (2020)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)Precise Learning Curves and Higher-Order Scalings for Dot-product Kernel Regression.L. Xiao, H. Hu, T. Misiakiewicz, Y. Lu, and J. Pennington. NeurIPS, (2022)

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Disambiguation of "Xiao, Lechao"

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Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes.

Please choose a person to relate this publication to

Fang Xiao

Wenjiang Xiao

Meifang Xiao

Jianping Xiao

Chun Xiao

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Xiao, Lechao"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes.

Please choose a person to relate this publication to

Fang Xiao

Wenjiang Xiao

Meifang Xiao

Jianping Xiao

Chun Xiao

Other publications of authors with the same name

Disambiguation

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URL
DOI
BibTeX
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APA
Chicago
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Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes.