@inproceedings{7e410988b5ef4943a98c9c36bed40ae2,
title = "Critical Slowing down near Topological Transitions in Rate-Distortion Problems",
abstract = "In rate-distortion (RD) problems one seeks reduced representations of a source that meet a target distortion constraint. Such optimal representations undergo topological transitions at some critical rate values, when their cardinality or dimensionality change. We study the convergence time of the Arimoto-Blahut alternating projection algorithms, used to solve such problems, near those critical points, both for the ratedistortion and information bottleneck settings. We argue that they suffer from critical slowing down - a diverging number of iterations for convergence - near the critical points. This phenomenon can have theoretical and practical implications for both machine learning and data compression problems.",
author = "Shlomi Agrnon and Etam Benger and Or Ordentlich and Naftali Tishby",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Symposium on Information Theory, ISIT 2021 ; Conference date: 12-07-2021 Through 20-07-2021",
year = "2021",
month = jul,
day = "12",
doi = "10.1109/ISIT45174.2021.9517956",
language = "الإنجليزيّة",
series = "IEEE International Symposium on Information Theory - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2625--2630",
booktitle = "2021 IEEE International Symposium on Information Theory, ISIT 2021 - Proceedings",
address = "الولايات المتّحدة",
}