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Learn2Hop: Learned Optimization on Rough Landscapes
[article]
2021
arXiv
pre-print
Optimization of non-convex loss surfaces containing many local minima remains a critical problem in a variety of domains, including operations research, informatics, and material design. Yet, current techniques either require extremely high iteration counts or a large number of random restarts for good performance. In this work, we propose adapting recent developments in meta-learning to these many-minima problems by learning the optimization algorithm for various loss landscapes. We focus on
arXiv:2107.09661v1
fatcat:lxtkaajmifdnpg7ww7gbk32jyq