Machine learning in a data-limited regime: Augmenting experiments with synthetic data uncovers order in crumpled sheets

Machine learning has gained widespread attention as a powerful tool to identify structure in complex, high-dimensional data. However, these techniques are ostensibly inapplicable for experimental systems where data are scarce or expensive to obtain. Here, we introduce a strategy to resolve this impa...

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Detalles Bibliográficos
Autores principales: Hoffmann, Jordan, Bar-Sinai, Yohai, Lee, Lisa M., Andrejevic, Jovana, Mishra, Shruti, Rubinstein, Shmuel M., Rycroft, Chris H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2019
Materias:
Research Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6486215/
https://www.ncbi.nlm.nih.gov/pubmed/31032399
http://dx.doi.org/10.1126/sciadv.aau6792

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6486215/
https://www.ncbi.nlm.nih.gov/pubmed/31032399
http://dx.doi.org/10.1126/sciadv.aau6792

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