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Collaborating Authors

 Attilio Fiandrotti


Learning sparse neural networks via sensitivity-driven regularization

Neural Information Processing Systems

The ever-increasing number of parameters in deep neural networks poses challenges for memory-limited applications. Regularize-and-prune methods aim at meeting these challenges by sparsifying the network weights. In this context we quantify the output sensitivity to the parameters (i.e.