Phase transitions in rate distortion theory and deep learning
DOI10.1007/s10208-021-09546-4OpenAlexW3212188481MaRDI QIDQ2684466
Andreas Klotz, Philipp Grohs, Felix Voigtlaender
Publication date: 16 February 2023
Published in: Foundations of Computational Mathematics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2008.01011
Sobolev spacesphase transitionBesov spacesneural network approximationapproximation ratesrate distortion theory
Artificial neural networks and deep learning (68T07) Coding and information theory (compaction, compression, models of communication, encoding schemes, etc.) (aspects in computer science) (68P30) Set functions and measures and integrals in infinite-dimensional spaces (Wiener measure, Gaussian measure, etc.) (28C20) Approximation by arbitrary nonlinear expressions; widths and entropy (41A46)
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