Stochastic generalized gradient methods for training nonconvex nonsmooth neural networks
type: Item
instance of: scholarly article
title: Stochastic generalized gradient methods for training nonconvex nonsmooth neural networks
zbMATH Open document ID: 1494.65047
author: Vladimir I. Norkin
published in: Cybernetics and Systems Analysis
publication date: 2021-12-09T00:00:00Z
Mathematics Subject Classification ID: 65K05
Mathematics Subject Classification ID: 68T07
Mathematics Subject Classification ID: 90C15
Mathematics Subject Classification ID: 90C26
zbMATH DE Number: 7441875
zbMATH Keywords: machine learning
zbMATH Keywords: deep learning
zbMATH Keywords: multilayer neural networks
zbMATH Keywords: nonsmooth nonconvex optimization
zbMATH Keywords: stochastic optimization
zbMATH Keywords: stochastic generalized gradient
MaRDI profile type: Publication
full work available at URL: s10559-021-00397-z
OpenAlex ID: W3204008412
cites work: Learning representations by back-propagating errors
cites work: Machine learning. The art and science of algorithms that make sense of data.
cites work: Q5270493
cites work: Optimization Methods for Large-Scale Machine Learning
cites work: A Stochastic Approximation Method
cites work: Robust Stochastic Approximation Approach to Stochastic Programming
cites work: Lectures on Stochastic Programming
cites work: Stochastic subgradient method converges on tame functions
cites work: Stochastic Model-Based Minimization of Weakly Convex Functions
cites work: Q3996569
cites work: Q3775322
cites work: Q3899415
cites work: Stochastic generalized gradient method for nonconvex nonsmooth stochastic optimization
cites work: Solution of nonconvex nonsmooth stochastic optimization problems
cites work: A Robust Gradient Sampling Algorithm for Nonsmooth, Nonconvex Optimization
cites work: Q4936225
cites work: Q3871832
cites work: An Algorithm for Constrained Optimization with Semismooth Functions
cites work: Tame functions are semismooth
cites work: Stochastic generalized-differentiable functions in the problem of nonconvex nonsmooth stochastic optimization
cites work: Generalized gradients in dynamic optimization, optimal control, and machine learning problems
cites work: Q3285814
cites work: Q3320132
cites work: Step control for direct stochastic-programming methods
cites work: Q4692293
cites work: Q4261789
cites work: Empirical risk minimization and problems of constructing linear classifiers
DOI: 10.1007/S10559-021-00397-Z
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description: scientific article; zbMATH DE number 7441875