Deep Learning Applied to Sign Language. - Jérôme Fink; Anthony Cleve; Benoît Frénay (BNAIC/BENELEARN, 2019)

Adapting Queries to Database Schema Changes in Hybrid Polystores. - Jérôme Fink; Maxime Gobert 001; Anthony Cleve (SCAM, 2020)

LSFB-CONT and LSFB-ISOL: Two New Datasets for Vision-Based Sign Language Recognition. - Jérôme Fink; Benoît Frénay; Laurence Meurant; Anthony Cleve (IJCNN, 2021)

Supporting policy-making with social media and e-participation platforms data: A policy analytics framework. - Anthony Simonofski; Jérôme Fink; Corentin Burnay (Gov. Inf. Q., 2021)

Trends and Challenges for Sign Language Recognition with Machine Learning. - Jérôme Fink; Mathieu De Coster; Joni Dambre; Benoît Frénay (ESANN, 2023)

Sign Language-to-Text Dictionary with Lightweight Transformer Models. - Jérôme Fink; Pierre Poitier; Maxime André 001; Loup Meurice; Benoît Frénay; Anthony Cleve; Bruno Dumas; Laurence Meurant (IJCAI, 2023)

Automated Risk Assessment of Shell-Based Attacks Using a LLM. - Sereysethy Touch; Jérôme Fink; Jean-Noël Colin (CRiSIS, 2024)

Leveraging endoscopic data with Contrastive Learning for Crohn's disease detection. - Robin Ghyselinck; Jérôme Fink; Bruno Dumas; Benoît Frénay (ESANN, 2024)

Towards better transition modeling in recurrent neural networks: The case of sign language tokenization. - Pierre Poitier; Jérôme Fink; Benoît Frénay (Neurocomputing, 2024)

Local-Global Data Augmentation for Contrastive Learning in Static Sign Language Recognition. - Ariel Basso Madjoukeng; Kenmogne Edith Belise; Pierre Poitier; Benoît Frénay; Jérôme Fink (IDA, 2025)