Publikationer från Malmö universitet
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Optimal Task Grouping Approach in Multitask Learning
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP). Halmstad Univ, Ctr Appl Intelligent Syst Res CAISR, Halmstad, Sweden..ORCID-id: 0000-0002-3797-4605
Qom Univ Technol, Fac Elect & Comp Engn, Qom, Iran..
Halmstad Univ, Ctr Appl Intelligent Syst Res CAISR, Halmstad, Sweden..
Halmstad Univ, Ctr Appl Intelligent Syst Res CAISR, Halmstad, Sweden..
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2024 (Engelska)Ingår i: Neural Information Processing: 30th International Conference, ICONIP 2023, Changsha, China, November 20–23, 2023, Proceedings, Part VI / [ed] Luo, B Wu, ZG Cheng, C Li, H Li, C, Springer, 2024, Vol. 14452, s. 206-225Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Multi-task learning has become a powerful solution in which multiple tasks are trained together to leverage the knowledge learned from one task to improve the performance of the other tasks. However, the tasks are not always constructive on each other in the multi-task formulation and might play negatively during the training process leading to poor results. Thus, this study focuses on finding the optimal group of tasks that should be trained together for multi-task learning in an automotive context. We proposed a multi-task learning approach to model multiple vehicle long-term behaviors using low-resolution data and utilized gradient descent to efficiently discover the optimal group of tasks/vehicle behaviors that can increase the performance of the predictive models in a single training process. In this study, we also quantified the contribution of individual tasks in their groups and to the other groups' performance. The experimental evaluation of the data collected from thousands of heavy-duty trucks shows that the proposed approach is promising.

Ort, förlag, år, upplaga, sidor
Springer, 2024. Vol. 14452, s. 206-225
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 14452
Nyckelord [en]
Machine Learning, Vehicle Usage Behavior, Multitask learning
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Datorsystem
Identifikatorer
URN: urn:nbn:se:mau:diva-66154DOI: 10.1007/978-981-99-8076-5_15ISI: 001148055700015ISBN: 978-981-99-8075-8 (tryckt)ISBN: 978-981-99-8076-5 (digital)OAI: oai:DiVA.org:mau-66154DiVA, id: diva2:1841073
Konferens
30th International Conference on Neural Information Processing (ICONIP) of the Asia-Pacific-Neural-Network-Society (APNNS), NOV 20-23, 2023, Changsha, PEOPLES R CHINA
Tillgänglig från: 2024-02-27 Skapad: 2024-02-27 Senast uppdaterad: 2024-02-27Bibliografiskt granskad

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Khoshkangini, Reza

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Institutionen för datavetenskap och medieteknik (DVMT)Internet of Things and People (IOTAP)
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Totalt: 17 träffar
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