Riemannian gradient methods for stochastic composition problems-Reference-Cited by-同舟云学术

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Riemannian gradient methods for stochastic composition problems

Published:2022-09 Issue: Volume:153 Page:224-234
ISSN:0893-6080
Container-title:Neural Networks
language:en
Short-container-title:Neural Networks

Author:

Huang Feihu^ORCID,Gao Shangqian

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference37 articles.

1. Manopt, a matlab toolbox for optimization on manifolds;Boumal;Journal of Machine Learning Research,2014

2. Solving stochastic compositional optimization is nearly as easy as solving stochastic optimization;Chen;IEEE Transactions on Signal Processing,2021

3. Momentum-based variance reduction in non-convex SGD;Cutkosky,2019

4. Natural neural networks;Desjardins,2015

5. Spider: Near-optimal non-convex optimization via stochastic path-integrated differential estimator;Fang,2018

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Gradient Descent Ascent for Minimax Problems on Riemannian Manifolds;IEEE Transactions on Pattern Analysis and Machine Intelligence;2023

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