Sparse Deep Neural Networks Using L1,∞-Weight Normalization

DOI10.5705/ss.202018.0468zbMath1479.62083OpenAlexW3037054256MaRDI QIDQ5155193

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Publication date: 6 October 2021

Published in: Statistica Sinica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.5705/ss.202018.0468

zbMATH Keywords

generalizationsparsityRademacher complexityoverfittingdeep neural networks

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Neural nets and related approaches to inference from stochastic processes (62M45) Statistical aspects of big data and data science (62R07)

Related Items

Improve robustness and accuracy of deep neural network with \(L_{2,\infty}\) normalization

Uses Software

• CIFAR
• ImageNet

Cites Work

• The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network
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