To address this dynamic requirement, we propose . Inspired by "Learn-to-Optimize" and "Learn-to-Hop" paradigms in meta-learning, we formulate the training process as a trajectory through a discrete space of loss functions. The core contributions of this paper are:
Use L2Hforadaptivity with f1, f3, f5 in systems like: l2hforadaptivity ef; f1 f3 f5