Learning with tree tensor networks: Complexity estimates and model selection-Reference-Cited by-同舟云学术

Learning with tree tensor networks: Complexity estimates and model selection

Published:2022-05-01 Issue:2 Volume:28 Page:
ISSN:1350-7265
Container-title:Bernoulli
language:
Short-container-title:Bernoulli

Author:

Michel Bertrand¹,Nouy Anthony¹

Affiliation:

1. Centrale Nantes, Laboratoire de Mathématiques Jean Leray, CNRS UMR 6629, France

Publisher

Bernoulli Society for Mathematical Statistics and Probability

Subject

Statistics and Probability

Reference43 articles.

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2. Ali, M. and Nouy, A. (2020). Approximation theory of tree tensor networks: Tensorized univariate functions. Part I. Preprint. Available at arxiv:2007.00118.

3. Ali, M. and Nouy, A. (2020). Approximation theory of tree tensor networks: Tensorized univariate functions. Part II. Preprint. Available at arxiv:2007.00128.

4. Ali, M. and Nouy, A. (2021). Approximation theory of tree tensor networks: Tensorized multivariate functions. Preprint. Available at arXiv:2101.11932.

5. Arlot, S. (2019). Minimal penalties and the slope heuristics: A survey. J. SFdS 160 1–106.

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