EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use case. - Natalia Díaz Rodríguez; Alberto Lamas 001; Jules Sanchez; Gianni Franchi; Ivan Donadello; Siham Tabik; David Filliat; Policarpo Cruz; Rosana Montes; Francisco Herrera (CoRR, 2021)

EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: The MonuMAI cultural heritage use case. - Natalia Díaz Rodríguez; Alberto Lamas 001; Jules Sanchez; Gianni Franchi; Ivan Donadello; Siham Tabik; David Filliat; Policarpo Cruz; Rosana Montes; Francisco Herrera (Inf. Fusion, 2022)

Domain generalization of 3D semantic segmentation in autonomous driving. - Jules Sanchez; Jean-Emmanuel Deschaud; François Goulette (ICCV, 2023)

COLA: COarse LAbel pre-training for 3D semantic segmentation of sparse LiDAR datasets. - Jules Sanchez; Jean-Emmanuel Deschaud; François Goulette (ICRA, 2023)

COLA: COarse-LAbel multi-source LiDAR semantic segmentation for autonomous driving. - Jules Sanchez; Jean-Emmanuel Deschaud; François Goulette (CoRR, 2023)

Généralisation de domaine pour la segmentation sémantique de données LiDAR pour le véhicule autonome. (Domain generalization for the semantic segmentation of LiDAR data for autonomous vehicles). - Jules Sanchez (2023)

ParisLuco3D: A High-Quality Target Dataset for Domain Generalization of LiDAR Perception. - Jules Sanchez; Louis Soum-Fontez; Jean-Emmanuel Deschaud; François Goulette (IEEE Robotics Autom. Lett., 2024)

COLA: COarse-LAbel Multisource LiDAR Semantic Segmentation for Autonomous Driving. - Jules Sanchez; Jean-Emmanuel Deschaud; François Goulette (IEEE Trans. Robotics, 2025)

3DLabelProp: Geometric-Driven Domain Generalization for LiDAR Semantic Segmentation in Autonomous Driving. - Jules Sanchez; Jean-Emmanuel Deschaud; François Goulette (CoRR, 2025)