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Author: wanglouis49

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+# Weights Pruning PyTorch Implementation
+
+Luyu Wang & Gavin Ding, Borealis AI
+
+----
+## Weights pruning 
+Han et al propose to compress deep learning models via weights pruning [Han et al, NIPS 2015](http://papers.nips.cc/paper/5784-learning-both-weights-and-connections-for-efficient-neural-network). This repo is an implementation in PyTorch. The pruning method is replaced by the "class-blinded" method mentioned in [See et al, CoNLL 2016](https://arxiv.org/abs/1606.09274), which is much easier to implement and has better performance as well.
+
+----
+## High-level idea
+1. We write wrappers on PyTorch Linear and Conv2d layers
+2. For each layer, once a binary mask tensor is computed, it is multiplied with the actual weights tensor on the forward pass
+3. Multiplying the mask is a differentiable operation and the backward pass is handed by automatic differentiation (no explicit code here)
+
+----
+## Notes
+This implementation is not aiming at obtaining computational efficiency but to offer convenience for studying properties of pruned networks. Discussions on how to have an efficient implementation is welcome. Thanks!