Emerging Relation Network and Task Embedding for Multi-Task Regression Problems

Jens Schreiber, Bernhard Sick

Multi-task learning (mtl) provides state-of-the-art results in many applications of computer vision and natural language processing. In contrast to single-task learning (stl), mtl allows for leveraging knowledge between related tasks improving prediction results on the main task (in contrast to an auxiliary task) or all tasks. However, there is a limited number of comparative studies on applying mtl architectures for regression and time series problems taking recent advances of mtl into account. An interesting, non-linear problem is the forecast of the expected power generation for renewable power plants. Therefore, this article provides a comparative study of the following recent and important mtl architectures: Hard parameter sharing, cross-stitch network, sluice network (sn). They are compared to a multi-layer perceptron model of similar size in an stl setting. Additionally, we provide a simple, yet effective approach to model task specific information through an embedding layer in an multi-layer perceptron, referred to as task embedding. Further, we introduce a new mtl architecture named emerging relation network (ern), which can be considered as an extension of the sluice network. For a solar power dataset, the task embedding achieves the best mean improvement with 14.9%. The mean improvement of the ern and the sn on the solar dataset is of similar magnitude with 14.7% and 14.8%. On a wind power dataset, only the ern achieves a significant improvement of up to 7.7%. Results suggest that the ern is beneficial when tasks are only loosely related and the prediction problem is more non-linear. Contrary, the proposed task embedding is advantageous when tasks are strongly correlated. Further, the task embedding provides an effective approach with reduced computational effort compared to other mtl architectures.

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