  This paper focuses on the data-insufficiency problem in multi-task learning
within an episodic training setup. Specifically, we explore the potential of
heterogeneous information across tasks and meta-knowledge among episodes to
effectively tackle each task with limited data. Existing meta-learning methods
often fail to take advantage of crucial heterogeneous information in a single
episode, while multi-task learning models neglect reusing experience from
earlier episodes. To address the problem of insufficient data, we develop
Heterogeneous Neural Processes (HNPs) for the episodic multi-task setup. Within
the framework of hierarchical Bayes, HNPs effectively capitalize on prior
experiences as meta-knowledge and capture task-relatedness among heterogeneous
tasks, mitigating data-insufficiency. Meanwhile, transformer-structured
inference modules are designed to enable efficient inferences toward
meta-knowledge and task-relatedness. In this way, HNPs can learn more powerful
functional priors for adapting to novel heterogeneous tasks in each meta-test
episode. Experimental results show the superior performance of the proposed
HNPs over typical baselines, and ablation studies verify the effectiveness of
the designed inference modules.
