Approximation Algorithms for Training One-Node ReLU Neural Networks

Training a one-node neural network with the ReLU activation function via optimization, which we refer to as the ON-ReLU problem, is a fundamental problem in machine learning. In this paper, we begin by proving the NP-hardness of the ON-ReLU problem. We then present an approximation algorithm to solv...

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Vydané v:	IEEE transactions on signal processing Ročník 68; s. 6696 - 6706
Hlavní autori:	Dey, Santanu S., Wang, Guanyi, Xie, Yao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Approximation Approximation algorithms Biological neural networks Machine learning Machine learning algorithms Neural networks Non-convex optimization Optimization Optimized production technology Performance enhancement Random noise Ratios Signal processing algorithms Training training neural networks
ISSN:	1053-587X, 1941-0476
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