Parsimonious Optimization of Multitask Neural Network Hyperparameters

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...

Full description

Bibliographic Details
Main Authors: Cecile Valsecchi, Viviana Consonni, Roberto Todeschini, Marco Emilio Orlandi, Fabio Gosetti, Davide Ballabio
Format: Article
Language:English
Published: MDPI AG 2021-11-01
Series:Molecules
Subjects:
neural networks
optimization
genetic algorithms
grid search
random search
tree-structured Parzen estimator
Online Access:https://www.mdpi.com/1420-3049/26/23/7254

Internet

https://www.mdpi.com/1420-3049/26/23/7254

Similar Items