7 citations
- Institut de Recherche pour le DéveloppementFR4 papers
- UMR Espace-Dev4 papers
- Université de MontpellierFR2 papers
- Aix-Marseille UniversitéFR1 paper
- American UniversityUS1 paper
- Bioversity InternationalCO1 paper
- Brookings InstitutionUS1 paper
- CARE USAUS1 paper
- Centre de Coopération Internationale en Recherche Agronomique pour le DéveloppementFR1 paper
- Centre National de la Recherche ScientifiqueFR1 paper
- Cornell UniversityUS1 paper
- Digital Research Alliance of Canada1 paper
5 papers
MixMAS: A Framework for Sampling-Based Mixer Architecture Search for Multimodal Fusion and Learning
Abdelmadjid Chergui, Grigor Bezirganyan, Sana Sellami +2
Choosing a suitable deep learning architecture for multimodal data fusion is a challenging task, as it requires the effective integration and processing of diverse data types, each…
Hierarchical Classification for Automated Image Annotation of Coral Reef Benthic Structures
Célia Blondin, Joris Guérin, Kelly Inagaki +2
Automated benthic image annotation is crucial to efficiently monitor and protect coral reefs against climate change. Current machine learning approaches fail to capture the hierarc…
Safety Monitoring of Machine Learning Perception Functions: a Survey
Raul Sena Ferreira, Joris Guérin, Kevin Delmas +2
Machine Learning (ML) models, such as deep neural networks, are widely applied in autonomous systems to perform complex perception tasks. New dependability challenges arise when ML…
The State of Food Systems Worldwide: Counting Down to 2030
Kate Schneider, Jessica Fanzo, Lawrence Haddad +54
Transforming food systems is essential to bring about a healthier, equitable, sustainable, and resilient future, including achieving global development and sustainability goals. To…
Discovering Multi-Table Functional Dependencies Without Full Join Computation
Ugo Comignani, Laure Berti-Équille, Noël Novelli
In this paper, we study the problem of discovering join FDs, i.e., functional dependencies (FDs) that hold on multiple joined tables. We leverage logical inference, selective minin…