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Data-driven emergence of convolutional structure in neural networks
Exploiting data invariances is crucial for efficient learning in both artificial and biological neural circuits. Understanding how neural networks can discover appropriate representations capable of harnessing the underlying symmetries of their inputs is thus crucial in machine learning and neurosci...
Autores principales: | Ingrosso, Alessandro, Goldt, Sebastian |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9546588/ https://www.ncbi.nlm.nih.gov/pubmed/36161906 http://dx.doi.org/10.1073/pnas.2201854119 |
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