341 citations · 360 across the 10 of their papers we have counts for
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ELSA: Partial Weight Freezing for Overhead-Free Sparse Network Deployment
Paniz Halvachi, Alexandra Peste, Dan Alistarh +1
We present ELSA, a practical solution for creating deep networks that can easily be deployed at different levels of sparsity. The core idea is to embed one or more sparse networks…
Accurate Neural Network Pruning Requires Rethinking Sparse Optimization
Denis Kuznedelev, Eldar Kurtic, Eugenia Iofinova +3
Obtaining versions of deep neural networks that are both highly-accurate and highly-sparse is one of the main challenges in the area of model compression, and several high-performa…
Knowledge Distillation Performs Partial Variance Reduction
Mher Safaryan, Alexandra Peste, Dan Alistarh
Knowledge distillation is a popular approach for enhancing the performance of ''student'' models, with lower representational capacity, by taking advantage of more powerful ''teach…
Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures
Eugenia Iofinova, Alexandra Peste, Dan Alistarh
Pruning - that is, setting a significant subset of the parameters of a neural network to zero - is one of the most popular methods of model compression. Yet, several recent works h…