all AI news
Useful Compact Representations for Data-Fitting
March 20, 2024, 4:42 a.m. | Johannes J. Brust
cs.LG updates on arXiv.org arxiv.org
Abstract: For minimization problems without 2nd derivative information, methods that estimate Hessian matrices can be very effective. However, conventional techniques generate dense matrices that are prohibitive for large problems. Limited-memory compact representations express the dense arrays in terms of a low rank representation and have become the state-of-the-art for software implementations on large deterministic problems. We develop new compact representations that are parameterized by a choice of vectors and that reduce to existing well known formulas …
abstract arrays art arxiv become cs.lg cs.na data express generate however information low math.na math.oc memory representation software stat.co state terms type
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Senior Data Engineer
@ Cint | Gurgaon, India
Data Science (M/F), setor automóvel - Aveiro
@ Segula Technologies | Aveiro, Portugal