From Latent Dynamics to Meaningful Representations

Author:

Wang Dedi1ORCID,Wang Yihang1,Evans Luke2,Tiwary Pratyush3ORCID

Affiliation:

1. Biophysics Program and Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, United States

2. Department of Mathematics, University of Maryland, College Park, Maryland 20742, United States

3. Department of Chemistry and Biochemistry and Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, United States

Funder

Basic Energy Sciences

Publisher

American Chemical Society (ACS)

Reference60 articles.

1. Representation Learning: A Review and New Perspectives

2. Schölkopf, B.; Janzing, D.; Peters, J.; Sgouritsa, E.; Zhang, K.; Mooij, J. On causal and anticausal learning. 2012, arXiv preprint arXiv:1206.6471.

3. Kalinin, S. V.; Zhang, S.; others Exploring particle dynamics during self-organization processes via rotationally invariant latent representations. 2020, arXiv preprint arXiv:2009.00783.

4. Ziatdinov, M.; Wong, C. Y.; Kalinin, S. V. Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders. 2021, arXiv preprint arXiv:2106.12472.

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