1 citations · 1 across the 9 of their papers we have counts for
11 papers
Capacity-Aware Deep Learning for Generalizable Traffic Volume Estimation Across Links and Cities
Léo Hein, Giovanni De Nunzio, Aurélie Pirayre +1
Network-wide traffic volume estimation typically relies on propagating measurements from fixed sensors, making performance highly dependent on sensor density and limiting deploymen…
Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention
Léo Hein, Giovanni de Nunzio, Giovanni Chierchia +2
Existing traffic volume estimation methods typically address either forecasting traffic on sensor-equipped roads or spatially imputing missing volumes using nearby sensors. While f…
Data Science: a Natural Ecosystem
Emilio Porcu, Roy El Moukari, Laurent Najman +2
This manuscript provides a systemic and data-centric view of what we term essential data science, as a natural ecosystem with challenges and missions stemming from the fusion of da…
FLIM-based Salient Object Detection Networks with Adaptive Decoders
Gilson Junior Soares, Matheus Abrantes Cerqueira, Jancarlo F. Gomes +3
Salient Object Detection (SOD) methods can locate objects that stand out in an image, assign higher values to their pixels in a saliency map, and binarize the map outputting a pred…
Evolutionary Retrofitting
Mathurin Videau, Mariia Zameshina, Alessandro Leite +3
AfterLearnER (After Learning Evolutionary Retrofitting) consists in applying evolutionary optimization to refine fully trained machine learning models by optimizing a set of carefu…
Log-normal Mutations and their Use in Detecting Surreptitious Fake Images
Ismail Labiad, Thomas Bäck, Pierre Fernandez +5
In many cases, adversarial attacks are based on specialized algorithms specifically dedicated to attacking automatic image classifiers. These algorithms perform well, thanks to an…
Quantile Activation: Correcting a Failure Mode of ML Models
Aditya Challa, Sravan Danda, Laurent Najman +1
Standard ML models fail to infer the context distribution and suitably adapt. For instance, the learning fails when the underlying distribution is actually a mixture of distributio…
A Novel Approach to Regularising 1NN classifier for Improved Generalization
Aditya Challa, Sravan Danda, Laurent Najman
In this paper, we propose a class of non-parametric classifiers, that learn arbitrary boundaries and generalize well. Our approach is based on a novel way to regularize 1NN classif…
Clustering Dynamics for Improved Speed Prediction Deriving from Topographical GPS Registrations
Sarah Almeida Carneiro, Giovanni Chierchia, Aurelie Pirayre +1
A persistent challenge in the field of Intelligent Transportation Systems is to extract accurate traffic insights from geographic regions with scarce or no data coverage. To this e…
PrivacyGAN: robust generative image privacy
Mariia Zameshina, Marlene Careil, Olivier Teytaud +1
Classical techniques for protecting facial image privacy typically fall into two categories: data-poisoning methods, exemplified by Fawkes, which introduce subtle perturbations to…
Diverse Diffusion: Enhancing Image Diversity in Text-to-Image Generation
Mariia Zameshina, Olivier Teytaud, Laurent Najman
Latent diffusion models excel at producing high-quality images from text. Yet, concerns appear about the lack of diversity in the generated imagery. To tackle this, we introduce Di…