RESEARCH
SPEECH & AUDIO
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning
November 18, 2019
Abstract
The application of stochastic variance reduction to optimization has shown remarkable recent theoretical and practical success. The applicability of these techniques to the hard non-convex optimization problems encountered during training of modern deep neural networks is an open problem. We show that naive application of the SVRG technique and related approaches fail, and explore why.
Download the Paper
AUTHORS
Publisher
NeurIPS
Related Publications
October 14, 2026
RESEARCH
Human-Guided Agentic AI for End-to-End EHR Research: Sequential Antibody-Drug Conjugate Pathways in Breast Cancer
Arturo Loaiza-Bonilla
October 14, 2026
October 14, 2026
RESEARCH
Pickleball injuries, search interest, and participation in the United States, 2014-2023: an ecological time series study
Daniel Kim
October 14, 2026
October 14, 2026
RESEARCH
Outcomes in Methicillin-Susceptible and Methicillin-Resistant Staphylococcus aureus Infective Endocarditis Compared With Staphylococcus lugdunensis Infective Endocarditis
Paddy Ssentongo
October 14, 2026
October 14, 2026
RESEARCH
Social Needs Z Codes and Subsequent Acute Care Across 21 U.S. Health Systems
Roberto Lobelo
October 14, 2026
Help Us Pioneer The Future of AI
We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.
Meta © 2026
