EuroTechTalk #5: Efficiently learning across open datasets: Cases from the transport domain
Автор: EuroTech Universities Alliance
Загружено: 2023-04-18
Просмотров: 104
Efficiently learning across open datasets has become increasingly important in the field of machine learning, and has important implications in the Transport field. Transfer learning and meta-learning are two popular techniques that allow models to leverage knowledge gained from one task and apply it to another. Transfer learning focuses on using pre-trained models as a starting point for new tasks, while meta-learning focuses on learning how to learn from as few examples as possible.
In this talk, Professors Francisco C. Pereira (Technical University of Denmark, DTU) and Constantinos Antoniou (Technical University of Munich, TUM) introduce both concepts in the context of Transport research. First, they discuss transfer learning for traffic state estimation using scalable and non-scalable data from large European cities, and then they introduce meta-learning for automated fleet rebalancing in Autonomous Mobility on Demand (AMoD) using taxi data from multiple cities.
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