2022
DOI: 10.48550/arxiv.2206.06322
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Abstract: Deep Multi-Task Learning (DMTL) has been widely studied in the machine learning community and applied to a broad range of real-world applications. Searching for the optimal knowledge sharing in DMTL is more challenging for sequential learning problems, as the task relationship will change in the temporal dimension. In this paper, we propose a flexible and efficient framework called Hierarchical-Temporal Activation Network (HTAN) to simultaneously explore the optimal sharing of the neural network hierarchy (hie… Show more

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