807 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Université de RennesFR75 papers
- Centre National de la Recherche ScientifiqueFR38 papers
- Université Paris-SaclayFR26 papers
- Université Paris CitéFR24 papers
- Université de MontréalCA23 papers
- Observatoire de la Côte d’AzurFR22 papers
- Polytechnique MontréalCA22 papers
- Sorbonne UniversitéFR22 papers
- University of BirminghamGB22 papers
- University of CambridgeGB22 papers
- University of MichiganUS22 papers
- University of PisaIT22 papers
7 papers · 1 filter
Domain adaptation for handwriting trajectory reconstruction from IMU sensors
Florent Imbert, Romain Tavenard, Yann Soullard +1
Digital pens are commonly used to write on digital devices, providing the handwriting trace and enhancing human-computer interation. This study focuses on a digital pen equipped wi…
Mixture-of-experts for handwriting trajectory reconstruction from IMU sensors
Florent Imbert, Eric Anquetil, Yann Soullard +1
The use of digital pens for online handwriting trajectory reconstruction is a prevalent method for human-computer interaction. In this study, we focus on a digital pen equipped wit…
A Gated Graph Neural Network Approach to Fast-Convergent Dynamic Average Estimation
Antonio Marino, Claudio Pacchierotti, Paolo Robuffo Giordano
Dynamic average estimation is a critical problem in multi-agent systems, enabling agents to collaboratively estimate time-varying signals using only local information exchange. Tra…
MANO: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under Distribution Shifts
Renchunzi Xie, Ambroise Odonnat, Vasilii Feofanov +3
Leveraging the models' outputs, specifically the logits, is a common approach to estimating the test accuracy of a pre-trained neural network on out-of-distribution (OOD) samples w…
Leveraging Gradients for Unsupervised Accuracy Estimation under Distribution Shift
Renchunzi Xie, Ambroise Odonnat, Vasilii Feofanov +3
Estimating the test performance of a model, possibly under distribution shift, without having access to the ground-truth labels is a challenging, yet very important problem for the…
Match-And-Deform: Time Series Domain Adaptation through Optimal Transport and Temporal Alignment
François Painblanc, Laetitia Chapel, Nicolas Courty +3
While large volumes of unlabeled data are usually available, associated labels are often scarce. The unsupervised domain adaptation problem aims at exploiting labels from a source…
Data-Centric Green AI: An Exploratory Empirical Study
Roberto Verdecchia, Luís Cruz, June Sallou +3
With the growing availability of large-scale datasets, and the popularization of affordable storage and computational capabilities, the energy consumed by AI is becoming a growing…