Deep Neural Network Approximation Theory

This paper develops fundamental limits of deep neural network learning by characterizing what is possible if no constraints are imposed on the learning algorithm and on the amount of training data. Concretely, we consider Kolmogorov-optimal approximation through deep neural networks with the guiding...

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Bibliographic Details
Published in:	IEEE transactions on information theory Vol. 67; no. 5; pp. 2581 - 2623
Main Authors:	Elbrachter, Dennis, Perekrestenko, Dmytro, Grohs, Philipp, Bolcskei, Helmut
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.05.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Approximation Approximation error Artificial neural networks Complexity Complexity theory Dictionaries Function approximation Function space Functions (mathematics) Machine learning Mathematical analysis metric entropy Multiplication Network topologies Neural networks nonlinear approximation Polynomials rate-distortion theory Training data Two dimensional displays
ISSN:	0018-9448, 1557-9654
Online Access:	Get full text
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