| Generalized multimodal ELBO TM Sutter, I Daunhawer, JE Vogt arXiv preprint arXiv:2105.02470, 2021 | 109 | 2021 |
| Multimodal Learning Utilizing Jensen-Shannon Divergence TM Sutter, I Daunhawer, JE Vogt Advances in Neural Information Processing Systems 33 pre-proceedings …, 2020 | 91* | 2020 |
| On the limitations of multimodal vaes I Daunhawer, TM Sutter, K Chin-Cheong, E Palumbo, JE Vogt arXiv preprint arXiv:2110.04121, 2021 | 40 | 2021 |
| Generation of Heterogeneous Synthetic Electronic Health Records using GANs K Chin-Cheong, TM Sutter, JE Vogt Workshop on Machine Learning for Health (ML4H) at the 33rd Conference on …, 2019 | 39 | 2019 |
| Self-supervised disentanglement of modality-specific and shared factors improves multimodal generative models I Daunhawer, TM Sutter, R Marcinkevičs, JE Vogt Pattern Recognition: 42nd DAGM German Conference, DAGM GCPR 2020, Tübingen …, 2021 | 33 | 2021 |
| Machine learning algorithms evaluate immune response to novel Mycobacterium tuberculosis antigens for diagnosis of tuberculosis NR Meier, N Ritz, TM Sutter, JE Vogt, THM Ottenhoff, M Jacobsen Frontiers in Cellular and Infection Microbiology 10, 821, 2020 | 15 | 2020 |
| A comparison of general and disease-specific machine learning models for the prediction of unplanned hospital readmissions TM Sutter, JA Roth, K Chin-Cheong, BL Hug, JE Vogt Journal of the American Medical Informatics Association, 2020 | 11 | 2020 |
| Generation of differentially private heterogeneous electronic health records K Chin-Cheong, T Sutter, JE Vogt arXiv preprint arXiv:2006.03423, 2020 | 9 | 2020 |
| Camera based visibility estimation T Sutter, F Nater, C Sigg Proceedings TECO–2016 (technical conference on meteorological and …, 2016 | 9 | 2016 |
| Learning Group Importance using the Differentiable Hypergeometric Distribution TM Sutter, L Manduchi, A Ryser, JE Vogt The Eleventh International Conference on Learning Representations, 2023 | 7 | 2023 |
| Differentiable Random Partition Models TM Sutter, A Ryser, J Liebeskind, JE Vogt arXiv preprint arXiv:2305.16841, 2023 | 3 | 2023 |
| Self-supervised Learning to Predict Ejection Fraction using Motion-mode Images Y Hu, TM Sutter, E Ozkan, JE Vogt 2023 ICLR First Workshop on {\textquotedblleft} Machine Learning {\&} Global …, 2023 | 3 | 2023 |
| Unity by Diversity: Improved Representation Learning for Multimodal VAEs T Sutter, Y Meng, A Agostini, D Chopard, N Fortin, J Vogt, B Shahbaba, ... Advances in Neural Information Processing Systems 37, 74262-74297, 2025 | 2 | 2025 |
| Multimodal Relational VAE TM Sutter, JE Vogt Neurips Workshop on Bayesian Deep Learning, 2021 | 2 | 2021 |
| M (otion)-Mode Based Prediction of Ejection Fraction Using Echocardiograms E Ozkan, TM Sutter, Y Hu, S Balzer, JE Vogt DAGM German Conference on Pattern Recognition, 307-320, 2023 | 1 | 2023 |
| Imposing and Uncovering Group Structure in Weakly-Supervised Learning TM Sutter ETH Zurich, 2023 | 1 | 2023 |
| M (otion)-mode Based Prediction of Cardiac Function on Echocardiograms TM Sutter, S Balzer, E Oezkan, JE Vogt ETH Zurich, 2022 | 1 | 2022 |
| From Pixels to Components: Eigenvector Masking for Visual Representation Learning A Bizeul, T Sutter, A Ryser, B Schölkopf, J von Kügelgen, JE Vogt arXiv preprint arXiv:2502.06314, 2025 | | 2025 |
| RadVLM: A Multitask Conversational Vision-Language Model for Radiology N Deperrois, H Matsuo, S Ruipérez-Campillo, M Vandenhirtz, S Laguna, ... arXiv preprint arXiv:2502.03333, 2025 | | 2025 |
| Learning Symmetric Contexts for Anomaly Detection A Ryser, TM Sutter, A Marx, JE Vogt NeurIPS 2024 Workshop on Symmetry and Geometry in Neural Representations, 2024 | | 2024 |
Julia E VogtETH Zurichبريد إلكتروني تم التحقق منه على inf.ethz.ch
