One-trial correction of legacy AI systems and stochastic separation theorems

DOI10.1016/J.INS.2019.02.001zbMath1448.68369arXiv1610.00494OpenAlexW2952773203MaRDI QIDQ2213103

Yanyan Li

Publication date: 27 November 2020

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1610.00494

zbMATH Keywords

machine learningbig dataseparation theoremsmeasure concentration

Mathematics Subject Classification ID

Inequalities; stochastic orderings (60E15) Geometric probability and stochastic geometry (60D05) Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05) General topics in artificial intelligence (68T01) Computational aspects of data analysis and big data (68T09)

Related Items (3)

Blessing of dimensionality at the edge and geometry of few-shot learning ⋮ Fast construction of correcting ensembles for legacy artificial intelligence systems: algorithms and a case study ⋮ Blessing of dimensionality: mathematical foundations of the statistical physics of data

Uses Software

• LIBLINEAR
• ImageNet
• AlexNet

Cites Work

• Constraint-defined manifolds: a legacy code approach to low-dimensional computation
• Isoperimetry of waists and concentration of maps
• Editorial: Randomized algorithms for training neural networks
• Approximation with random bases: pro et contra
• Extensions of Lipschitz mappings into a Hilbert space
• On extreme points of regular convex sets
• Metric structures for Riemannian and non-Riemannian spaces. Transl. from the French by Sean Michael Bates. With appendices by M. Katz, P. Pansu, and S. Semmes. Edited by J. LaFontaine and P. Pansu
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: One-trial correction of legacy AI systems and stochastic separation theorems