Bounds on the Approximation Power of Feedforward Neural Networks

Mohammad Mehrabi; Aslan Tchamkerten; Mansoor I. Yousefi

Bounds on the Approximation Power of Feedforward Neural Networks

Sharif University of Technology

Résultats de recherche: Contribution à un journal › Article de conférence › Revue par des pairs

Résumé

The approximation power of general feedforward neural networks with piecewise linear activation functions is investigated. First, lower bounds on the size of a network are established in terms of the approximation error and network depth and width. These bounds improve upon state-of-the-art bounds for certain classes of functions, such as strongly convex functions. Second, an upper bound is established on the difference of two neural networks with identical weights but different activation functions.

langue originale	Anglais
Pages (de - à)	3453-3461
Nombre de pages	9
journal	Proceedings of Machine Learning Research
Volume	80
état	Publié - 1 janv. 2018
Evénement	35th International Conference on Machine Learning, ICML 2018 - Stockholm, Sucde Durée: 10 juil. 2018 → 15 juil. 2018

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abstract = "The approximation power of general feedforward neural networks with piecewise linear activation functions is investigated. First, lower bounds on the size of a network are established in terms of the approximation error and network depth and width. These bounds improve upon state-of-the-art bounds for certain classes of functions, such as strongly convex functions. Second, an upper bound is established on the difference of two neural networks with identical weights but different activation functions.",

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N2 - The approximation power of general feedforward neural networks with piecewise linear activation functions is investigated. First, lower bounds on the size of a network are established in terms of the approximation error and network depth and width. These bounds improve upon state-of-the-art bounds for certain classes of functions, such as strongly convex functions. Second, an upper bound is established on the difference of two neural networks with identical weights but different activation functions.

AB - The approximation power of general feedforward neural networks with piecewise linear activation functions is investigated. First, lower bounds on the size of a network are established in terms of the approximation error and network depth and width. These bounds improve upon state-of-the-art bounds for certain classes of functions, such as strongly convex functions. Second, an upper bound is established on the difference of two neural networks with identical weights but different activation functions.

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SN - 2640-3498

VL - 80

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JO - Proceedings of Machine Learning Research

JF - Proceedings of Machine Learning Research

T2 - 35th International Conference on Machine Learning, ICML 2018

Y2 - 10 July 2018 through 15 July 2018

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Bounds on the Approximation Power of Feedforward Neural Networks

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