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Multi-task learning by pareto optimality

Dyankov, D., Riccio, S. D., Di Fatta, G. and Nicosia, G. (2019) Multi-task learning by pareto optimality. In: Machine Learning, Optimization, and Data Science. LOD 2019. Lecture Notes in Computer Science (11943). Springer, pp. 605-618. ISBN 9783030375997

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To link to this item DOI: 10.1007/978-3-030-37599-7_50


Deep Neural Networks (DNNs) are often criticized because they lack the ability to learn more than one task at a time: Multitask Learning is an emerging research area whose aim is to overcome this issue. In this work, we introduce the Pareto Multitask Learning framework as a tool that can show how effectively a DNN is learning a shared representation common to a set of tasks. We also experimentally show that it is possible to extend the optimization process so that a single DNN simultaneously learns how to master two or more Atari games: using a single weight parameter vector, our network is able to obtain sub-optimal results for up to four games.

Item Type:Book or Report Section
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:89524

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