Multi-Task Learning via Time-Aware Neural ODE
- Publisher:
- International Joint Conferences on Artificial Intelligence
- Publication Type:
- Conference Proceeding
- Citation:
- IJCAI International Joint Conference on Artificial Intelligence, 2023, 2023-August, pp. 4495-4503
- Issue Date:
- 2023-01-01
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Multi Task Learning MTL is a well established paradigm for learning shared models for a diverse set of tasks Moreover MTL improves data efficiency by jointly training all tasks simultaneously However directly optimizing the losses of all the tasks may lead to imbalanced performance on all the tasks due to the competition among tasks for the shared parameters in MTL models Many MTL methods try to mitigate this problem by dynamically weighting task losses or manipulating task gradients Different from existing studies in this paper we propose a Neural Ordinal diffeRential equation based Multi tAsk Learning NORMAL method to alleviate this issue by modeling task specific feature transformations from the perspective of dynamic flows built on the Neural Ordinary Differential Equation NODE Specifically the proposed NORMAL model designs a time aware neural ODE block to learn task specific time information which determines task positions of feature transformations in the dynamic flow in NODE automatically via gradient descent methods In this way the proposed NORMAL model handles the problem of competing shared parameters by learning task positions Moreover the learned task positions can be used to measure the relevance among different tasks Extensive experiments show that the proposed NORMAL model outperforms state of the art MTL models 2023 International Joint Conferences on Artificial Intelligence All rights reserved
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