Parsimonious Optimization of Multitask Neural Network Hyperparameters

Neural networks are rapidly gaining popularity in chemical modeling and Quantitative Structure–Activity Relationship (QSAR) thanks to their ability to handle multitask problems. However, outcomes of neural networks depend on the tuning of several hyperparameters, whose small variations can often str...

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Detalles Bibliográficos
Autores principales: Valsecchi, Cecile, Consonni, Viviana, Todeschini, Roberto, Orlandi, Marco Emilio, Gosetti, Fabio, Ballabio, Davide
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8658836/
https://www.ncbi.nlm.nih.gov/pubmed/34885837
http://dx.doi.org/10.3390/molecules26237254

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8658836/
https://www.ncbi.nlm.nih.gov/pubmed/34885837
http://dx.doi.org/10.3390/molecules26237254

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