Learning Interacting Theories from Data-Reference-Cited by-同舟云学术

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Learning Interacting Theories from Data

Published:2023-11-20 Issue:4 Volume:13 Page:
ISSN:2160-3308
Container-title:Physical Review X
language:en
Short-container-title:Phys. Rev. X

Author:

Merger Claudia^ORCID,René Alexandre^ORCID,Fischer Kirsten^ORCID,Bouss Peter^ORCID,Nestler Sandra^ORCID,Dahmen David^ORCID,Honerkamp Carsten,Helias Moritz^ORCID

Funder

Bundesministerium für Bildung und Forschung

Jülich Supercomputing Centre, Forschungszentrum Jülich

Deutsche Forschungsgemeinschaft

ERS

Helmholtz Association Initiative and Networking Fund

Publisher

American Physical Society (APS)

Subject

General Physics and Astronomy

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevX.13.041033/fulltext

Reference63 articles.

1. Scale-invariant representation of machine learning

2. Separability and geometry of object manifolds in deep neural networks

3. Maximal relevance and optimal learning machines

4. Modeling the Influence of Data Structure on Learning in Neural Networks: The Hidden Manifold Model

5. Decomposing neural networks as mappings of correlation functions

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Neural Activity in Quarks Language: Lattice Field Theory for a Network of Real Neurons;Entropy;2024-06-06

2. Spatio-temporal transformers for decoding neural movement control;2024-04-19

3. Inferring interpretable dynamical generators of local quantum observables from projective measurements through machine learning;Physical Review Applied;2024-04-03

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