Multitask Learning for Quantitative Structure–Activity Relationships: A Tutorial
Machine Learning and Deep Learning in Computational Toxicology, 2023•Springer
Multitask learning allows to model multiple tasks simultaneously through information
sharing. In the context of quantitative structure–activity relationships and computational
toxicology, multitask learning is gaining more and more interest, owed to its potential to
improve the predictive performance of underrepresented tasks and to predict the multi-
property profile of molecules. In this chapter, after introducing the multitask problem
formulation, we present a hands-on tutorial on multitask neural networks.
sharing. In the context of quantitative structure–activity relationships and computational
toxicology, multitask learning is gaining more and more interest, owed to its potential to
improve the predictive performance of underrepresented tasks and to predict the multi-
property profile of molecules. In this chapter, after introducing the multitask problem
formulation, we present a hands-on tutorial on multitask neural networks.
Abstract
Multitask learning allows to model multiple tasks simultaneously through information sharing. In the context of quantitative structure–activity relationships and computational toxicology, multitask learning is gaining more and more interest, owed to its potential to improve the predictive performance of underrepresented tasks and to predict the multi-property profile of molecules. In this chapter, after introducing the multitask problem formulation, we present a hands-on tutorial on multitask neural networks.
Springer
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