Routing the Lottery: Adaptive Subnetworks for Heterogeneous Data

This paper introduces Runtime Task Learning (RTL), an adaptive AI method that enables models to dynamically adjust their architectures based on incoming heterogeneous data. It demonstrates significant advancements in areas like image classification and speech enhancement, moving away from a 'one model fits all' approach to provide tailored solutions and efficiency gains, achieving accuracy improvements of up to 5% on CIFAR-100 benchmarks.