28 citations · 33 across the 9 of their papers we have counts for
11 papers
Hoeffding Concept Bottleneck Models with Applications to Overhead Images
Clément Bénard, Manon Arfib, Christophe Labreuche +1
Explainability of deep learning algorithms is critical for computer-vision applications with high-stakes decisions. Concept bottleneck models (CBM) have recently shown promising pe…
FOLDER: Accelerating Multi-modal Large Language Models with Enhanced Performance
Haicheng Wang, Zhemeng Yu, Gabriele Spadaro +4
Recently, Multi-modal Large Language Models (MLLMs) have shown remarkable effectiveness for multi-modal tasks due to their abilities to generate and understand cross-modal data. Ho…
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…
The Quest of Finding the Antidote to Sparse Double Descent
Victor Quétu, Marta Milovanović
In energy-efficient schemes, finding the optimal size of deep learning models is very important and has a broad impact. Meanwhile, recent studies have reported an unexpected phenom…
Can Unstructured Pruning Reduce the Depth in Deep Neural Networks?
Zhu Liao, Victor Quétu, Van-Tam Nguyen +1
Pruning is a widely used technique for reducing the size of deep neural networks while maintaining their performance. However, such a technique, despite being able to massively com…
Sparse Double Descent in Vision Transformers: real or phantom threat?
Victor Quétu, Marta Milovanovic, Enzo Tartaglione
Vision transformers (ViT) have been of broad interest in recent theoretical and empirical works. They are state-of-the-art thanks to their attention-based approach, which boosts th…
DSD: Can We Dodge Sparse Double Descent and Compress the Neural Network Worry-Free?
Victor Quétu, Enzo Tartaglione
Neoteric works have shown that modern deep learning models can exhibit a sparse double descent phenomenon. Indeed, as the sparsity of the model increases, the test performance firs…
Can we avoid Double Descent in Deep Neural Networks?
Victor Quétu, Enzo Tartaglione
Finding the optimal size of deep learning models is very actual and of broad impact, especially in energy-saving schemes. Very recently, an unexpected phenomenon, the ``double desc…