Structured Ensembles: An approach to reduce the memory footprint of ensemble methods.

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第一作者： Jary,Pomponi

第一单位： Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Italy. Electronic address: jary.pomponi@uniroma1.it.

作者： Jary,Pomponi ^[1] ; Simone,Scardapane ^[2] ; Aurelio,Uncini ^[2]

作者单位： Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Italy. Electronic address: jary.pomponi@uniroma1.it. ^[1] Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Italy. ^[2]

关键词 Continual learning Deep learning Ensemble Neural networks Pruning Structured pruning

主题词学习(Learning);偏差(Uncertainty)

DOI 10.1016/j.neunet.2021.09.007

PMID 34562814

发布时间 2021-11-24

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