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To prune, or not to prune: exploring the efficacy of pruning for model compression
[article]
2017
arXiv
pre-print
Model pruning seeks to induce sparsity in a deep neural network's various connection matrices, thereby reducing the number of nonzero-valued parameters in the model. Recent reports (Han et al., 2015; Narang et al., 2017) prune deep networks at the cost of only a marginal loss in accuracy and achieve a sizable reduction in model size. This hints at the possibility that the baseline models in these experiments are perhaps severely over-parameterized at the outset and a viable alternative for
arXiv:1710.01878v2
fatcat:kzsphmwc4rdvdmubyhnlpvplcy