multi-source-learning

GRASP proposes a method for multi-source transfer learning that sequentially merges source models into a single target model with constant O(1) memory usage, using gradient-based parameter alignment to avoid negative transfer. Experiments show it outperforms ensemble methods while being much more memory-efficient.