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A Classification of $G$-invariant Shallow Neural Networks. (arXiv:2205.09219v4 [cs.LG] UPDATED)
Aug. 17, 2022, 1:11 a.m. | Devanshu Agrawal, James Ostrowski
stat.ML updates on arXiv.org arxiv.org
When trying to fit a deep neural network (DNN) to a $G$-invariant target
function with $G$ a group, it only makes sense to constrain the DNN to be
$G$-invariant as well. However, there can be many different ways to do this,
thus raising the problem of "$G$-invariant neural architecture design": What is
the optimal $G$-invariant architecture for a given problem? Before we can
consider the optimization problem itself, we must understand the search space,
the architectures in it, and how …
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