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We present a Task-aware modulation using Representation Learning (TAM-RL)framework that enhances personalized predictions in few-shot settings forheterogeneous systems when individual task characteristics are not known.TAM-RL extracts embeddings representing the actual inherent characteristics ofthese entities and uses these characteristics to personalize the predictionsfor each entity/task. Using real-world hydrological and flux tower benchmarkdata sets, we show that TAM-RL can significantly outperform existing baselineapproaches such as MAML and multi-modal MAML (MMAML) while being much fasterand simpler to train due to less complexity. Specifically, TAM-RL eliminatesthe need for sensitive hyper-parameters like inner loop steps and inner looplearning rate, which are crucial for model convergence in MAML, MMAML. Wefurther present an empirical evaluation via synthetic data to explore theimpact of heterogeneity amongst the entities on the relative performance ofMAML, MMAML, and TAM-RL. We show that TAM-RL significantly improves predictiveperformance for cases where it is possible to learn distinct representationsfor different tasks.

Task Aware Modulation using Representation Learning: An Approach for Few Shot Learning in Heterogeneous Systems