Multi-task learning by pareto optimalityDyankov, 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 Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1007/978-3-030-37599-7_50 Abstract/SummaryDeep 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.
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