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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...
| Autores principales: | , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
American Association for the Advancement of Science
2019
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| Materias: | |
| 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 |