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Jun, 2021
可变等变神经网络的可定向偏微分算子
Steerable Partial Differential Operators for Equivariant Neural Networks
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Erik Jenner, Maurice Weiler
TL;DR
研究在相等变深度学习和物理领域中共享一致的数学概念,并通过极化微分算子和基于调和分析的方法提出了一个新的框架,使我们能够更好地将基于显式对称性应变的捕捉无论是在深度学习还是物理学中都可以更精确地进行表达。
Abstract
Recent work in
equivariant deep learning
bears strong similarities to
physics
. Fields over a base space are fundamental entities in both subjects, as are
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