221 citations · 507 across the 42 of their papers we have counts for
10 papers · 1 filter
SCoTTi: Save Computation at Training Time with an adaptive framework
Ziyu Lin, Enzo Tartaglione, Van-Tam Nguyen
On-device training is an emerging approach in machine learning where models are trained on edge devices, aiming to enhance privacy protection and real-time performance. However, ed…
Enhanced EEG-Based Mental State Classification : A novel approach to eliminate data leakage and improve training optimization for Machine Learning
Maxime Girard, Rémi Nahon, Enzo Tartaglione +1
In this paper, we explore prior research and introduce a new methodology for classifying mental state levels based on EEG signals utilizing machine learning (ML). Our method propos…
Towards On-device Learning on the Edge: Ways to Select Neurons to Update under a Budget Constraint
Aël Quélennec, Enzo Tartaglione, Pavlo Mozharovskyi +1
In the realm of efficient on-device learning under extreme memory and computation constraints, a significant gap in successful approaches persists. Although considerable effort has…
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…
Mining bias-target Alignment from Voronoi Cells
Rémi Nahon, Van-Tam Nguyen, Enzo Tartaglione
Despite significant research efforts, deep neural networks are still vulnerable to biases: this raises concerns about their fairness and limits their generalization. In this paper,…
Learn how to Prune Pixels for Multi-view Neural Image-based Synthesis
Marta Milovanović, Enzo Tartaglione, Marco Cagnazzo +1
Image-based rendering techniques stand at the core of an immersive experience for the user, as they generate novel views given a set of multiple input images. Since they have shown…
Optimized preprocessing and Tiny ML for Attention State Classification
Yinghao Wang, Rémi Nahon, Enzo Tartaglione +2
In this paper, we present a new approach to mental state classification from EEG signals by combining signal processing techniques and machine learning (ML) algorithms. We evaluate…
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…