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2 - Generalization in Deep Learning

Published online by Cambridge University Press:  29 November 2022

By
K. Kawaguchi ,
Y. Bengio  and
L. Kaelbling
Edited by
Philipp Grohs  and
Gitta Kutyniok
Philipp Grohs
Affiliation:
Universität Wien, Austria
Gitta Kutyniok
Affiliation:
Ludwig-Maximilians-Universität Munchen
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Summary

This chapter provides theoreticalinsights into why and how deep learning can generalize well, despite its large capacity, complexity, possible algorithmic instability, non-robustness, and sharp minima, responding to an open question in the literature. We also discuss approaches to provide non-vacuousgeneralization guarantees for deep learning. On the basis of the theoreticalobservations, wepropose new open problems.

Keywords

learning theoryneural networks
Type
Chapter
Information
Mathematical Aspects of Deep Learning , pp. 112 - 148
Publisher: Cambridge University Press
Print publication year: 2022

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