Self-supervised Disentanglement of Modality-Specific and Shared Factors Improves Multimodal Generative Models. - Imant Daunhawer; Thomas M. Sutter; Ricards Marcinkevics; Julia E. Vogt (GCPR, 2020)

Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence. - Thomas M. Sutter; Imant Daunhawer; Julia E. Vogt (NeurIPS, 2020)

Generation of Differentially Private Heterogeneous Electronic Health Records. - Kieran Chin-Cheong; Thomas M. Sutter; Julia E. Vogt (CoRR, 2020)

Generalized Multimodal ELBO. - Thomas M. Sutter; Imant Daunhawer; Julia E. Vogt (ICLR, 2021)

A comparison of general and disease-specific machine learning models for the prediction of unplanned hospital readmissions. - Thomas M. Sutter; Jan A. Roth; Kieran Chin-Cheong; Balthasar L. Hug; Julia E. Vogt (J. Am. Medical Informatics Assoc., 2021)

On the Limitations of Multimodal VAEs. - Imant Daunhawer; Thomas M. Sutter; Kieran Chin-Cheong; Emanuele Palumbo; Julia E. Vogt (ICLR, 2022)

Continuous Relaxation For The Multivariate Non-Central Hypergeometric Distribution. - Thomas M. Sutter; Laura Manduchi; Alain Ryser; Julia E. Vogt (CoRR, 2022)

M(otion)-Mode Based Prediction of Ejection Fraction Using Echocardiograms. - Ece Ozkan; Thomas M. Sutter; Yurong Hu; Sebastian Balzer; Julia E. Vogt (DAGM, 2023)

Learning Group Importance using the Differentiable Hypergeometric Distribution. - Thomas M. Sutter; Laura Manduchi; Alain Ryser; Julia E. Vogt (ICLR, 2023)

Differentiable Random Partition Models. - Thomas M. Sutter; Alain Ryser; Joram Liebeskind; Julia E. Vogt (NeurIPS, 2023)

Imposing and Uncovering Group Structure in Weakly-Supervised Learning. - Thomas M. Sutter (2023)

Can Generative AI Learn Physiological Waveform Morphologies? A Study on Denoising Intracardiac Signals in Ischemic Cardiomyopathy. - Samuel Ruipérez-Campillo; Alain Ryser; Thomas M. Sutter; Ruibin Feng; Prasanth Ganesan; Brototo Deb; Kelly A. Brennan; Maarten Z. Kolk; Fleur V. Y. Tjong; Albert J. Rogers; Sanjiv M. Narayan; Julia E. Vogt (EMBC, 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)

Unity by Diversity: Improved Representation Learning in Multimodal VAEs. - Thomas M. Sutter; Yang Meng; Norbert Fortin; Julia E. Vogt; Stephan Mandt (CoRR, 2024)

Anomaly Detection by Context Contrasting. - Alain Ryser; Thomas M. Sutter; Alexander Marx 001; 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)

RadVLM: A Multitask Conversational Vision-Language Model for Radiology. - Nicolas Deperrois; Hidetoshi Matsuo; Samuel Ruipérez-Campillo; Moritz Vandenhirtz; Sonia Laguna; Alain Ryser; Koji Fujimoto; Mizuho Nishio; Thomas M. Sutter; Julia E. Vogt; Jonas Kluckert; Thomas Frauenfelder; et al. (CoRR, 2025)

From Pixels to Components: Eigenvector Masking for Visual Representation Learning. - Alice Bizeul; Thomas M. Sutter; Alain Ryser; Bernhard Schölkopf; Julius von Kügelgen; Julia E. Vogt (CoRR, 2025)

Leveraging the Structure of Medical Data for Improved Representation Learning. - Andrea Agostini; Sonia Laguna; Alain Ryser; Samuel Ruipérez-Campillo; Moritz Vandenhirtz; Nicolas Deperrois; Farhad Nooralahzadeh; Michael Krauthammer; Thomas M. Sutter; Julia E. Vogt (CoRR, 2025)

Predicting Pulmonary Hypertension in Newborns: A Multi-view VAE Approach. - Lucas Erlacher; Samuel Ruipérez-Campillo; Holger Michel; Sven Wellmann; Thomas M. Sutter; Ece Ozkan; Julia E. Vogt (CoRR, 2025)

A Denoising VAE for Intracardiac Time Series in Ischemic Cardiomyopathy. - Samuel Ruipérez-Campillo; Alain Ryser; Thomas M. Sutter; Ruibin Feng; Prasanth Ganesan; Brototo Deb; Kelly A. Brennan; Maxime Pedron; Albert J. Rogers; Maarten Z. Kolk; Fleur V. Y. Tjong; Sanjiv M. Narayan; et al. (CoRR, 2025)