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.
Zero-Shot Adaptation for mmWave Beam-Tracking on Overhead Messenger Wires through Robust Adversarial Reinforcement Learning
Graduate School of Informatics, Kyoto University（京都大学信息学研究生院）；Centre for Wireless Communications, University of Oulu（奥卢大学无线通信中心）；School of Engineering, Tokyo Institute of Technology（东京工业大学工学院）；NTT Access Network Service Systems Laboratories, NTT Corporation（日本电报电话公司接入网业务系统实验室）
MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer Tasks
Hefei Institutes of Physical Science, Chinese Academy of Sciences（中国科学院合肥物质科学研究院）；University of Science and Technology of China（中国科学技术大学）；University of Amsterdam（阿姆斯特丹大学）；United Imaging Healthcare（联影医疗）
Dual-Branch Residual Network for Cross-Domain Few-Shot Hyperspectral Image Classification with Refined Prototype
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications（重庆邮电大学通信与信息工程学院）；School of Intelligent Systems Engineering, Sun Yat-sen University（中山大学智能系统工程学院）
MCLRL: A Multi-Domain Contrastive Learning with Reinforcement Learning Framework for Few-Shot Modulation Recognition
Institute of Cyberspace Security, College of Information Engineering, Zhejiang University of Technology（浙江工业大学信息工程学院网络空间安全研究所）；Binjiang Institute of Artificial Intelligence, Zhejiang University of Technology（浙江工业大学滨江人工智能研究院）；College of Information and Communication Engineering, Harbin Engineering University（哈尔滨工程大学信息与通信工程学院）
Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5
College of Engineering, University of Georgia（佐治亚大学工程学院）；Information System Department, UMBC（马里兰大学巴尔的摩郡分校信息系统系）；College of Graduate and Professional Studies, Trine University（特瑞大学研究生与专业研究学院）；School of Professional Studies, Columbia University（哥伦比亚大学专业研究学院）
The Chinese University of Hong Kong（香港中文大学）；Tsinghua University（清华大学）；Huawei Noah’s Ark Lab（华为诺亚实验室）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
