all AI news
A New Random Reshuffling Method for Nonsmooth Nonconvex Finite-sum Optimization
May 1, 2024, 4:43 a.m. | Junwen Qiu, Xiao Li, Andre Milzarek
cs.LG updates on arXiv.org arxiv.org
Abstract: Random reshuffling techniques are prevalent in large-scale applications, such as training neural networks. While the convergence and acceleration effects of random reshuffling-type methods are fairly well understood in the smooth setting, much less studies seem available in the nonsmooth case. In this work, we design a new normal map-based proximal random reshuffling (norm-PRR) method for nonsmooth nonconvex finite-sum problems. We show that norm-PRR achieves the iteration complexity $O(n^{-1/3}T^{-2/3})$ where $n$ denotes the number of component …
abstract applications arxiv case convergence cs.lg design effects math.oc networks neural networks optimization random scale studies sum training type while work
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Software Engineer for AI Training Data (School Specific)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Python)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Tier 2)
@ G2i Inc | Remote
Data Engineer
@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US