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1. Learning Task Relational Structure for Multi-task Feature Learning

   https://doi.org/10.1109/ICDM.2016.0166  

   Wang, De ; Nie, Feiping ; Huang, Heng ( December 2016 , IEEE International Conference on Data Mining (ICDM 2016))
2. 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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3. Termination checking and task decomposition for task-based intermittent programs

   https://doi.org/10.1145/3178372.3179525  

   Colin, Alexei ; Lucia, Brandon ( February 2018 , Proceedings of the 27th International Conference on Compiler Construction)
4. Nonverbal task learning

   Kunda, Maithilee ( January 2019 , Proceedings of the Seventh Annual Conference on Advances in Cognitive Systems)

   null (Ed.)

   Nonverbal task learning is defined here as a variant of interactive task learning in which an agent learns the definition of a new task without any verbal information such as task instructions. Instead, the agent must 1) learn the task definition using only a single solved example problem as its training input, and then 2) generalize this definition in order to successfully parse new problems. In this paper, we present a conceptual framework for nonverbal task learning, and we compare and contrast this type of learning with existing learning paradigms in AI. We also discuss nonverbal task learning in the context of nonverbal human intelligence tests, which are standardized tests designed to be given without any verbal instructions so that they can be used by people with language difficulties. 

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5. Adversarial task assignment

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