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2. Adapting Task Difficulty in a Cup-Stacking Rehabilitative Task

   https://doi.org/10.1145/3610978.3640558  

   Daniilidis, Melina; Dennler, Nathaniel Steele; Matarić, Maja; Nikolaidis, Stefanos (March 2024, ACM)
3. Multi-Label Multi-Task Learning with Dynamic Task Weight Balancing

   https://doi.org/10.1109/IRI49571.2020.00042  

   Wang, Tianyi; Chen, Shu-Ching (August 2020, 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI))

   null (Ed.)

   Data collected from real-world environments often contain multiple objects, scenes, and activities. In comparison to single-label problems, where each data sample only defines one concept, multi-label problems allow the co-existence of multiple concepts. To exploit the rich semantic information in real-world data, multi-label classification has seen many applications in a variety of domains. The traditional approaches to multi-label problems tend to have the side effects of increased memory usage, slow model inference speed, and most importantly the under-utilization of the dependency across concepts. In this paper, we adopt multi-task learning to address these challenges. Multi-task learning treats the learning of each concept as a separate job, while at the same time leverages the shared representations among all tasks. We also propose a dynamic task balancing method to automatically adjust the task weight distribution by taking both sample-level and task-level learning complexities into consideration. Our framework is evaluated on a disaster video dataset and the performance is compared with several state-of-the-art multi-label and multi-task learning techniques. The results demonstrate the effectiveness and supremacy of our approach. 

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   Colin, Alexei; Lucia, Brandon (February 2018, Proceedings of the 27th International Conference on Compiler Construction)
5. Adversarial task assignment

   Hajaj, C; Vorobeychik, Y (January 2018, International Joint Conference on Artificial Intelligence)