Thomas M. Sutter

According to our database, Thomas M. Sutter authored at least 12 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Unity by Diversity: Improved Representation Learning in Multimodal VAEs.

Thomas M. Sutter, Yang Meng, Norbert Fortin, Julia E. Vogt, Stephan Mandt

CoRR, 2024

2023

Imposing and Uncovering Group Structure in Weakly-Supervised Learning.

Thomas M. Sutter

PhD thesis, 2023

Differentiable Random Partition Models.

Thomas M. Sutter, Alain Ryser, Joram Liebeskind, Julia E. Vogt

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning Group Importance using the Differentiable Hypergeometric Distribution.

Thomas M. Sutter, Laura Manduchi, Alain Ryser, Julia E. Vogt

Proceedings of the Eleventh International Conference on Learning Representations, 2023

M(otion)-Mode Based Prediction of Ejection Fraction Using Echocardiograms.

Ece Ozkan, Thomas M. Sutter, Yurong Hu, Sebastian Balzer, Julia E. Vogt

Proceedings of the Pattern Recognition - 45th DAGM German Conference, 2023

2022

Continuous Relaxation For The Multivariate Non-Central Hypergeometric Distribution.

Thomas M. Sutter, Laura Manduchi, Alain Ryser, Julia E. Vogt

CoRR, 2022

On the Limitations of Multimodal VAEs.

Imant Daunhawer, Thomas M. Sutter, Kieran Chin-Cheong, Emanuele Palumbo, Julia E. Vogt

Proceedings of the Tenth International Conference on Learning Representations, 2022

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

Generalized Multimodal ELBO.

Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Generation of Differentially Private Heterogeneous Electronic Health Records.

Kieran Chin-Cheong, Thomas M. Sutter, Julia E. Vogt

CoRR, 2020

Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence.

Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Self-supervised Disentanglement of Modality-Specific and Shared Factors Improves Multimodal Generative Models.

Imant Daunhawer, Thomas M. Sutter, Ricards Marcinkevics, Julia E. Vogt

Proceedings of the Pattern Recognition - 42nd DAGM German Conference, DAGM GCPR 2020, Tübingen, Germany, September 28, 2020