A concave-regularized relaxed binary mask, pruned by threshold and rewound, finds sparse trainable subnetworks with accuracy comparable to or better than iterative magnitude pruning.
Pruning convolutional neural networks for resource efficient inference,
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Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks
A concave-regularized relaxed binary mask, pruned by threshold and rewound, finds sparse trainable subnetworks with accuracy comparable to or better than iterative magnitude pruning.