12 citations · 12 across the 5 of their papers we have counts for
5 papers
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…
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…
Improving tracking with a tracklet associator
Rémi Nahon, Guillaume-Alexandre Bilodeau, Gilles Pesant
Multiple object tracking (MOT) is a task in computer vision that aims to detect the position of various objects in videos and to associate them to a unique identity. We propose an…