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

Arvind Renganathan, Rahul Ghosh, Ankush Khandelwal, Vipin Kumar

TL;DR

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, which offers substantial computational efficiency.

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.

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

TL;DR

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, which offers substantial computational efficiency.

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.
Paper Structure (43 sections, 6 equations, 8 figures, 9 tables, 2 algorithms)

This paper contains 43 sections, 6 equations, 8 figures, 9 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem Formulation
  4. Motivating Problems
  5. Method
  6. Modulation Network:
  7. Base Network:
  8. Modulation:
  9. Training
  10. Inference:
  11. Advantages of TAM-RL:
  12. Experiments
  13. Datasets:
  14. Baselines:
  15. Implementation Details:
  16. ...and 28 more sections

Figures (8)

  • Figure 1: (a) Forward modeling for an entity/physical system with known characteristics. (b) TAM-RL caricature
  • Figure 2: Comparison of MAML, MMAML, and TAM-RL a) MAML computes single meta-initialization (red dot), which is adapted(curved arrows) to tasks(blue dots). MAML fails to adapt to dissimilar tasks (outer blue dots) using few shots of data b) MMAMLvuorio2019multimodal uses task-specific modulation parameters to modulate (straight arrow) meta-initialization (red dot) and get task specific initialization (orange dot) which are then fine-tuned to tasks(blue dots). c) TAM-RL uses task-specific modulation parameters to modulate (straight arrow) shared initialization (red dot) to directly compute meta initialization (orange dots) for given tasks(blue dots).
  • Figure 3: TAM-RL Architecture. The modulation network generates task-specific modulation parameters $\tau$, which are used to adapt the base network. This diagram illustrates the architecture when the base network is an LSTM.
  • Figure 4: Few-shot prediction setting followed in this paper.
  • Figure 5: Performance comparison of various models regarding memory usage, training time, and RMSE on the Fluxnet dataset.
  • ...and 3 more figures