221 citations · 507 across the 42 of their papers we have counts for
7 papers · 2 filters
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…
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…
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,…
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…