Tutorial

Sparsity in Deep Learning: Pruning and growth for efficient inference and training

Torsten Hoefler · Dan Alistarh

Abstract:

This tutorial will perform an detailed overview of the work on sparsity in deep learning, covering sparsifi- cation techniques for neural networks, from both the mathematical and implementation perspectives. We specifically aim to cover the significant recent advances in the area, and put them in the context of the foundational work performed on this topic in the 1990s.

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