Web: http://arxiv.org/abs/2110.04227

Jan. 31, 2022, 2:11 a.m. | Michael Puthawala, Matti Lassas, Ivan Dokmanić, Maarten de Hoop

cs.LG updates on arXiv.org arxiv.org

We study approximation of probability measures supported on $n$-dimensional
manifolds embedded in R^m by injective flows -- neural networks composed of
invertible flows and injective layers. We show that in general, injective flows
between R^n and R^m universally approximate measures supported on images of
extendable embeddings, which are a subset of standard embeddings: when the
embedding dimension m is small, topological obstructions may preclude certain
manifolds as admissible targets. When the embedding dimension is sufficiently
large, m \geq 3n+1, we …


More from arxiv.org / cs.LG updates on arXiv.org

Data Scientist

@ Fluent, LLC | Boca Raton, Florida, United States

Big Data ETL Engineer

@ Binance.US | Vancouver

Data Scientist / Data Engineer

@ Kin + Carta | Chicago

Data Engineer

@ Craft | Warsaw, Masovian Voivodeship, Poland

Senior Manager, Data Analytics Audit

@ Affirm | Remote US

Data Scientist - Nationwide Opportunities, AWS Professional Services

@ Amazon.com | US, NC, Virtual Location - N Carolina