Volume 410 -
The 5th International Workshop on Deep Learning in Computational Physics
(DLCP2021) -
Regular papers
Equivariant Gaussian Processes as Limiting Convolutional Networks with Infinite Number of Channels
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Pre-published on:
December 01, 2021
Published on:
January 12, 2022
Abstract
In this work we establish a relationship between the many-channel limit for $SO(2)$-equivariant convolutional neural networks (CNNs) and the corresponding equivariant Gaussian process (GP) in the case of Fourier space quadratic nonlinearity. The approach used provides explicit equivariance at each step of the relationship derivation.
DOI: https://doi.org/10.22323/1.410.0002
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Copyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.