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• W2949273893 abstract "We revisit the idea of brain damage, i.e. the pruning of the coefficients of a neural network, and suggest how brain damage can be modified and used to speedup convolutional layers. The approach uses the fact that many efficient implementations reduce generalized convolutions to matrix multiplications. The suggested brain damage process prunes the convolutional kernel tensor in a group-wise fashion by adding group-sparsity regularization to the standard training process. After such group-wise pruning, convolutions can be reduced to multiplications of thinned dense matrices, which leads to speedup. In the comparison on AlexNet, the method achieves very competitive performance." @default.
• W2949273893 created "2019-06-27" @default.
• W2949273893 creator A5025519425 @default.
• W2949273893 creator A5083458209 @default.
• W2949273893 date "2015-06-08" @default.
• W2949273893 modified "2023-09-27" @default.
• W2949273893 title "Fast ConvNets Using Group-wise Brain Damage" @default.
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• W2949273893 hasPublicationYear "2015" @default.
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