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pro vyhledávání: '"Irto, Daniele"'
Publikováno v:
Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:88-98 (2023)
Pruning deep neural networks is a widely used strategy to alleviate the computational burden in machine learning. Overwhelming empirical evidence suggests that pruned models retain very high accuracy even with a tiny fraction of parameters. However,
Externí odkaz:
http://arxiv.org/abs/2306.12190
