28 citations · 45 across the 20 of their papers we have counts for
20 papers
How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection
Lorenzo Guerra, Thomas Chapuis, Guillaume Duc +2
Provenance-based intrusion detection systems (PIDS) frequently report strong performance, but the conclusions drawn from these results can be highly sensitive to benchmarking choic…
Predicting Only from Selected Evidence: A Tempered Product-of-Experts Bottleneck for Auditable EEG Diagnosis
Yinghao Wang, Shujian Yu, Duc Han Le +3
Pretrained EEG backbones improve transfer performance, but downstream diagnosis heads remain hard to audit: predictions are made from unrestricted hidden states, whereas explanatio…
HFMCA: Orthonormal Feature Learning for EEG-based Brain Decoding
Yinghao Wang, Lintao Xu, Shujian Yu +2
Electroencephalography (EEG) analysis is critical for brain-computer interfaces and neuroscience, but the intrinsic noise and high dimensionality of EEG signals hinder effective fe…
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…
Efficient Resource-Constrained Training of Transformers via Subspace Optimization
Le-Trung Nguyen, Enzo Tartaglione, Van-Tam Nguyen
As AI increasingly shapes daily life, energy consumption and data privacy have become pressing concerns. On-device learning trains models directly on edge devices, cutting energy c…
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…
TolerantECG: A Foundation Model for Imperfect Electrocardiogram
Huynh Dang Nguyen, Trong-Thang Pham, Ngan Le +1
The electrocardiogram (ECG) is an essential and effective tool for diagnosing heart diseases. However, its effectiveness can be compromised by noise or unavailability of one or mor…
Beyond Low-rank Decomposition: A Shortcut Approach for Efficient On-Device Learning
Le-Trung Nguyen, Ael Quelennec, Van-Tam Nguyen +1
On-device learning has emerged as a promising direction for AI development, particularly because of its potential to reduce latency issues and mitigate privacy risks associated wit…
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…
AI-Driven Intrusion Detection Systems (IDS) on the ROAD Dataset: A Comparative Analysis for Automotive Controller Area Network (CAN)
Lorenzo Guerra, Linhan Xu, Paolo Bellavista +4
The integration of digital devices in modern vehicles has revolutionized automotive technology, enhancing safety and the overall driving experience. The Controller Area Network (CA…
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…
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…
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…