Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data

In many machine learning applications, one uses pre-trained neural networks, having limited access to training and test data. Martin et al. show how to predict trends in the quality of such neural networks without access to this information, relevant for reproducibility, diagnostics, and validation.

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Detalles Bibliográficos
Autores principales: Charles H. Martin, Tongsu (Serena) Peng, Michael W. Mahoney
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/bdc9afbd811d47888c4645cf78e0b595
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