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

TL;DR

TAM-RL is a novel meta-learning framework that efficiently adapts to heterogeneous environmental tasks with limited data, significantly improving prediction accuracy and computational speed over existing methods.

Contribution

The paper introduces TAM-RL, a new multimodal meta-learning approach that leverages representation learning and task-aware modulation for few-shot environmental modeling.

Findings

Improves RMSE by 18.9% on GPP prediction with one month of data.

Achieves 8.21% better streamflow forecasting accuracy with one year of data.

Offers at least 3x faster training times than gradient-based meta-learning methods.

Abstract

We introduce TAM-RL (Task Aware Modulation using Representation Learning), a novel multimodal meta-learning framework for few-shot learning in heterogeneous systems, designed for science and engineering problems where entities share a common underlying forward model but exhibit heterogeneity due to entity-specific characteristics. TAM-RL leverages an amortized training process with a modulation network and a base network to learn task-specific modulation parameters, enabling efficient adaptation to new tasks with limited data. We evaluate TAM-RL on two real-world environmental datasets: Gross Primary Product (GPP) prediction and streamflow forecasting, demonstrating significant improvements over existing meta-learning methods. On the FLUXNET dataset, TAM-RL improves RMSE by 18.9\% over MMAML with just one month of few-shot data, while for streamflow prediction, it achieves an 8.21\% improvement with one year of data. Synthetic data experiments further validate TAM-RL's superior performance in heterogeneous task distributions, outperforming the baselines in the most heterogeneous setting. Notably, TAM-RL offers substantial computational efficiency, with at least 3x faster training times compared to gradient-based meta-learning approaches while being much simpler to train due to reduced complexity. Ablation studies highlight the importance of pretraining and adaptation mechanisms in TAM-RL's performance.