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Learning to learn from data: Using deep adversarial learning to construct optimal statistical procedures
Traditionally, statistical procedures have been derived via analytic calculations whose validity often relies on sample size growing to infinity. We use tools from deep learning to develop a new approach, adversarial Monte Carlo meta-learning, for constructing optimal statistical procedures. Statist...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
American Association for the Advancement of Science
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7051830/ https://www.ncbi.nlm.nih.gov/pubmed/32166115 http://dx.doi.org/10.1126/sciadv.aaw2140 |