Data-driven emergence of convolutional structure in neural networks

Exploiting data invariances is crucial for efficient learning in both artificial and biological neural circuits. Understanding how neural networks can discover appropriate representations capable of harnessing the underlying symmetries of their inputs is thus crucial in machine learning and neurosci...

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Bibliographic Details

Published in:	Proceedings of the National Academy of Sciences - PNAS Vol. 119; no. 40; p. e2201854119
Main Authors:	Ingrosso, Alessandro, Goldt, Sebastian
Format:	Journal Article
Language:	English
Published:	United States 04.10.2022
Subjects:	Machine Learning Neural Networks, Computer Neurosciences convolution receptive fields invariance neural networks
ISSN:	1091-6490, 1091-6490
Online Access:	Get more information
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