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• W3208452612 abstract "The discovery of sparse subnetworks that are able to perform as well as full models has found broad applied and theoretical interest. While many pruning methods have been developed to this end, the naive approach of removing parameters based on their magnitude has been found to be as robust as more complex, state-of-the-art algorithms. The lack of theory behind magnitude pruning's success, especially pre-convergence, and its relation to other pruning methods, such as gradient based pruning, are outstanding open questions in the field that are in need of being addressed. We make use of recent advances in dynamical systems theory, namely Koopman operator theory, to define a new class of theoretically motivated pruning algorithms. We show that these algorithms can be equivalent to magnitude and gradient based pruning, unifying these seemingly disparate methods, and that they can be used to shed light on magnitude pruning's performance during early training." @default.
• W3208452612 created "2021-11-08" @default.
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• W3208452612 date "2021-10-28" @default.
• W3208452612 modified "2023-09-26" @default.
• W3208452612 title "An Operator Theoretic Perspective on Pruning Deep Neural Networks" @default.
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• W3208452612 hasPublicationYear "2021" @default.
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