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Complexity from Adaptive-Symmetries Breaking: Global Minima in the Statistical Mechanics of Deep Neural Networks. (arXiv:2201.07934v1 [cs.LG])
cs.LG updates on arXiv.org arxiv.org
An antithetical concept, adaptive symmetry, to conservative symmetry in
physics is proposed to understand the deep neural networks (DNNs). It
characterizes the invariance of variance, where a biotic system explores
different pathways of evolution with equal probability in absence of feedback
signals, and complex functional structure emerges from quantitative
accumulation of adaptive-symmetries breaking in response to feedback signals.
Theoretically and experimentally, we characterize the optimization process of a
DNN system as an extended adaptive-symmetry-breaking process. One particular
finding is that …
arxiv complexity global networks neural networks statistical