activity
20182023
most citedSparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks

341 citations · 360 across the 10 of their papers we have counts for

collaborators
Showing cs.CVShow all

2 papers · 1 filter

cs.CV2023

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…

cs.CV2021

How Well Do Sparse Imagenet Models Transfer?

Eugenia Iofinova, Alexandra Peste, Mark Kurtz +1

Transfer learning is a classic paradigm by which models pretrained on large "upstream" datasets are adapted to yield good results on "downstream" specialized datasets. Generally, m…