Classification d'objets 3D par extraction aléatoire de sous-parties discriminantes pour l'étude du sous-sol en prospection pétrolière. - François Meunier; Christophe Marsala; Laurent Castanie; Bruno Conche (EGC, 2017)

Topologically Controlled Lossy Compression. - Maxime Soler; Mélanie Plainchault; Bruno Conche; Julien Tierny (PacificVis, 2018)

Lifted Wasserstein Matcher for Fast and Robust Topology Tracking. - Maxime Soler; Mélanie Plainchault; Bruno Conche; Julien Tierny (LDAV, 2018)

Use of Remote Sensing Radar Images for Offshore Oil Slick Detection in Oil and Gas Domain: Manual and Automatic Interpretation. - Véronique Miegebielle; Bruno Conche; Zhexuan Huang; Peigen Xie; Clément Killisly (IGARSS, 2019)

Ranking Viscous Finger Simulations to an Acquired Ground Truth with Topology-Aware Matchings. - Maxime Soler; Martin Petitfrere; Gilles Darche; Mélanie Plainchault; Bruno Conche; Julien Tierny (LDAV, 2019)

Offshore Oil Slicks Detection From SAR Images Through The Mask-RCNN Deep Learning Model. - Emna Amri; Alexandre Benoît; Philippe Bolon; Véronique Miegebielle; Bruno Conche; Georges Oppenheim (IJCNN, 2020)

An efficient and flexible inference system for serving heterogeneous ensembles of deep neural networks. - Pierrick Pochelu; Serge G. Petiton; Bruno Conche (IEEE BigData, 2021)

Distributed Ensembles of Reinforcement Learning Agents for Electricity Control. - Pierrick Pochelu; Bruno Conche; Serge G. Petiton (COINS, 2022)

A Deep Neural Networks ensemble workflow from hyperparameter search to inference leveraging GPU clusters. - Pierrick Pochelu; Serge G. Petiton; Bruno Conche (HPC Asia, 2022)

Weakly Supervised Faster-RCNN+FPN to classify animals in camera trap images. - Pierrick Pochelu; Clara Erard; Philippe Cordier; Serge G. Petiton; Bruno Conche (IVSP, 2022)