Binarized neural networks converge toward algorithmic simplicity: empirical support for the learning-as-compression hypothesis
Understanding and controlling the complexity of neural networks is a central challenge in machine learning, with implications for generalization, optimization, and model capacity. While most approaches rely on entropy-based loss functions and statistical metrics, these measures often fail to capture...
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| Principais autores: | , , , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Frontiers Media S.A.
2026-05-01
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| coleção: | Frontiers in Computational Neuroscience |
| Assuntos: | |
| Acesso em linha: | https://www.frontiersin.org/articles/10.3389/fncom.2026.1791546/full |
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