221 citations · 507 across the 42 of their papers we have counts for
7 papers · 2 filters
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…
Activation Map Compression through Tensor Decomposition for Deep Learning
Le-Trung Nguyen, Aël Quélennec, Enzo Tartaglione +2
Internet of Things and Deep Learning are synergetically and exponentially growing industrial fields with a massive call for their unification into a common framework called Edge AI…
Memory-Optimized Once-For-All Network
Maxime Girard, Victor Quétu, Samuel Tardieu +2
Deploying Deep Neural Networks (DNNs) on different hardware platforms is challenging due to varying resource constraints. Besides handcrafted approaches aiming at making deep model…
LaCoOT: Layer Collapse through Optimal Transport
Victor Quétu, Zhu Liao, Nour Hezbri +2
Although deep neural networks are well-known for their outstanding performance in tackling complex tasks, their hunger for computational resources remains a significant hurdle, pos…
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…
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…
Debiasing surgeon: fantastic weights and how to find them
Rémi Nahon, Ivan Luiz De Moura Matos, Van-Tam Nguyen +1
Nowadays an ever-growing concerning phenomenon, the emergence of algorithmic biases that can lead to unfair models, emerges. Several debiasing approaches have been proposed in the…