A Deep Learning Approach to Self-expansion of Abbreviations Based on Morphology and Context Distance. - Daphné Chopard; Irena Spasic (SLSP, 2019)

Simulation and annotation of global acronyms. - Maxim Filimonov; Daphné Chopard; Irena Spasic (Bioinform., 2022)

Deep Generative Clustering with Multimodal Diffusion Variational Autoencoders. - Emanuele Palumbo; Laura Manduchi; Sonia Laguna; Daphné Chopard; Julia E. Vogt (ICLR, 2024)

Unity by Diversity: Improved Representation Learning for Multimodal VAEs. - Thomas M. Sutter; Yang Meng; Andrea Agostini; Daphné Chopard; Norbert Fortin; Julia E. Vogt; Babak Shahbaba; Stephan Mandt (NeurIPS, 2024)

Automatic Classification of General Movements in Newborns. - Daphné Chopard; Sonia Laguna; Kieran Chin-Cheong; Annika Dietz; Anna Badura; Sven Wellmann; Julia E. Vogt (CoRR, 2024)

Weakly-Supervised Multimodal Learning on MIMIC-CXR. - Andrea Agostini; Daphné Chopard; Yang Meng; Norbert Fortin; Babak Shahbaba; Stephan Mandt; Thomas M. Sutter; Julia E. Vogt (CoRR, 2024)

Towards Foundation Models for Critical Care Time Series. - Manuel Burger; Fedor Sergeev; Malte Londschien; Daphné Chopard; Hugo Yèche; Eike Gerdes; Polina Leshetkina; Alexander Morgenroth; Zeynep Babür; Jasmina Bogojeska; Martin Faltys; Rita Kuznetsova; et al. (CoRR, 2024)

Word sense disambiguation of acronyms in clinical narratives. - Daphné Chopard; Padraig Corcoran; Irena Spasic (Frontiers Digit. Health, 2024)