2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 2 cited
Till the Layers Collapse: Compressing a Deep Neural Network through the Lenses of Batch Normalization Layers
Zhu Liao, Nour Hezbri, Victor Quétu +2
Today, deep neural networks are widely used since they can handle a variety of complex tasks. Their generality makes them very powerful tools in modern technology. However, deep ne…
cs.LG2024★ 1 cited
The Simpler The Better: An Entropy-Based Importance Metric To Reduce Neural Networks' Depth
Victor Quétu, Zhu Liao, Enzo Tartaglione
While deep neural networks are highly effective at solving complex tasks, large pre-trained models are commonly employed even to solve consistently simpler downstream tasks, which…
cs.LG2024
Layer Collapse Can be Induced by Unstructured Pruning
Zhu Liao, Victor Quétu, Van-Tam Nguyen +1
Unstructured pruning is a popular compression method for efficiently reducing model parameters. However, while it effectively decreases the number of parameters, it is commonly bel…