44 citations · 107 across the 16 of their papers we have counts for
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cs.LG2022★ 2 cited
Where to Pay Attention in Sparse Training for Feature Selection?
Ghada Sokar, Zahra Atashgahi, Mykola Pechenizkiy +1
A new line of research for feature selection based on neural networks has recently emerged. Despite its superiority to classical methods, it requires many training iterations to co…
cs.LG2022★ 34 cited
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training
Shiwei Liu, Tianlong Chen, Xiaohan Chen +4
Random pruning is arguably the most naive way to attain sparsity in neural networks, but has been deemed uncompetitive by either post-training pruning or sparse training. In this p…