24 citations · 157 across the 42 of their papers we have counts for
8 papers · 1 filter
Memory Constrained Dynamic Subnetwork Update for Transfer Learning
Aël Quélennec, Pavlo Mozharovskyi, Van-Tam Nguyen +1
On-device neural network training faces critical memory constraints that limit the adaptation of pre-trained models to downstream tasks. We present MeDyate, a theoretically-grounde…
Study of Training Dynamics for Memory-Constrained Fine-Tuning
Aël Quélennec, Nour Hezbri, Pavlo Mozharovskyi +2
Memory-efficient training of deep neural networks has become increasingly important as models grow larger while deployment environments impose strict resource constraints. We propo…
Self-Supervised Learning of Graph Representations for Network Intrusion Detection
Lorenzo Guerra, Thomas Chapuis, Guillaume Duc +2
Detecting intrusions in network traffic is a challenging task, particularly under limited supervision and constantly evolving attack patterns. While recent works have leveraged gra…
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…
Tailoring Mixup to Data for Calibration
Quentin Bouniot, Pavlo Mozharovskyi, Florence d'Alché-Buc
Among all data augmentation techniques proposed so far, linear interpolation of training samples, also called Mixup, has found to be effective for a large panel of applications. Al…
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…
Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications
Morgane Goibert, Stéphan Clémençon, Ekhine Irurozki +1
The concept of median/consensus has been widely investigated in order to provide a statistical summary of ranking data, i.e. realizations of a random permutation of a finite se…
A Framework to Learn with Interpretation
Jayneel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc
To tackle interpretability in deep learning, we present a novel framework to jointly learn a predictive model and its associated interpretation model. The interpreter provides both…