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ORCIDThomas M. Sutter
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2020 – today
- 2024
[i10]Thomas M. Sutter, Yang Meng, Norbert Fortin, Julia E. Vogt, Stephan Mandt:
Unity by Diversity: Improved Representation Learning in Multimodal VAEs. CoRR abs/2403.05300 (2024)- [i9]Alain Ryser, Thomas M. Sutter, Alexander Marx
, Julia E. Vogt:
Anomaly Detection by Context Contrasting. CoRR abs/2405.18848 (2024) - 2023
- [b1]Thomas M. Sutter:
Imposing and Uncovering Group Structure in Weakly-Supervised Learning. ETH Zurich, Zürich, Switzerland, 2023 - [c7]Ece Ozkan, Thomas M. Sutter, Yurong Hu, Sebastian Balzer, Julia E. Vogt:
M(otion)-Mode Based Prediction of Ejection Fraction Using Echocardiograms. DAGM 2023: 307-320 - [c6]Thomas M. Sutter, Laura Manduchi, Alain Ryser, Julia E. Vogt:
Learning Group Importance using the Differentiable Hypergeometric Distribution. ICLR 2023 - [c5]Thomas M. Sutter, Alain Ryser, Joram Liebeskind, Julia E. Vogt:
Differentiable Random Partition Models. NeurIPS 2023 - [i8]Thomas M. Sutter, Alain Ryser, Joram Liebeskind, Julia E. Vogt:
Differentiable Random Partition Models. CoRR abs/2305.16841 (2023) - [i7]Ece Ozkan, Thomas M. Sutter, Yurong Hu, Sebastian Balzer, Julia E. Vogt:
M(otion)-mode Based Prediction of Ejection Fraction using Echocardiograms. CoRR abs/2309.03759 (2023) - 2022
- [c4]Imant Daunhawer, Thomas M. Sutter, Kieran Chin-Cheong, Emanuele Palumbo, Julia E. Vogt:
On the Limitations of Multimodal VAEs. ICLR 2022 - [i6]Thomas M. Sutter, Laura Manduchi, Alain Ryser, Julia E. Vogt:
Continuous Relaxation For The Multivariate Non-Central Hypergeometric Distribution. CoRR abs/2203.01629 (2022) - 2021
- [j1]Thomas M. Sutter, Jan A. Roth, Kieran Chin-Cheong, Balthasar L. Hug, Julia E. Vogt:
A comparison of general and disease-specific machine learning models for the prediction of unplanned hospital readmissions. J. Am. Medical Informatics Assoc. 28(4): 868-873 (2021) - [c3]Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt:
Generalized Multimodal ELBO. ICLR 2021 - [i5]Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt:
Generalized Multimodal ELBO. CoRR abs/2105.02470 (2021) - [i4]Imant Daunhawer, Thomas M. Sutter, Kieran Chin-Cheong, Emanuele Palumbo, Julia E. Vogt:
On the Limitations of Multimodal VAEs. CoRR abs/2110.04121 (2021) - 2020
- [c2]Imant Daunhawer, Thomas M. Sutter, Ricards Marcinkevics, Julia E. Vogt:
Self-supervised Disentanglement of Modality-Specific and Shared Factors Improves Multimodal Generative Models. GCPR 2020: 459-473 - [c1]Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt:
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence. NeurIPS 2020 - [i3]Kieran Chin-Cheong, Thomas M. Sutter, Julia E. Vogt:
Generation of Differentially Private Heterogeneous Electronic Health Records. CoRR abs/2006.03423 (2020) - [i2]Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt:
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence. CoRR abs/2006.08242 (2020)
2010 – 2019
- 2019
- [i1]Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt:
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence. ViGIL@NeurIPS 2019
Coauthor Index
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