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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About Me
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Posts
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Blog Post number 4
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Blog Post number 1
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portfolio
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publications
Generation of differentially private heterogeneous electronic health records
Published in Arxiv, 2020
Generating differentially private medical records.
Recommended citation: K Chin-Cheong, TM Sutter, JE Vogt. (2020). "Generation of Differentially Private Heterogeneous Electronic Health Records." arxiv preprint. https://arxiv.org/abs/2006.03423
Self-supervised disentanglement of modality-specific and shared factors improves multimodal generative model
Published in GCPR 2020, 2020
Disentangling shared and modality-specific latent factors in multimodal data.
Recommended citation: I Daunhawer, TM Sutter, R Marcinkevics, JE Vogt. (2020). "Self-supervised Disentanglement of Modality-Specific and Shared Factors Improves Multimodal Generative Models." GCPR 2020. https://link.springer.com/chapter/10.1007/978-3-030-71278-5_33
Multimodal Generative Learning Utilizing the Jensen-Shannon Divergence
Published in Neurips 2020, 2020
Utilizing the Jensen-Shannon divergence in a VAE setting to learn from multiple data types.
Recommended citation: TM Sutter, I Daunhawer, JE Vogt. (2020). "Multimodal Generative Learning Utilizing the Jensen-Shannon Divergence." Neurips 2020. https://proceedings.neurips.cc/paper/2020/hash/43bb733c1b62a5e374c63cb22fa457b4-Abstract.html
A comparison of general and disease-specific machine learning models for the prediction of unplanned hospital readmissions
Published in Journal of the American Medical Informatics Association, 2020
Comparing different models with respect to their predictive performance for the detection of unplanned early readmissions.
Recommended citation: TM Sutter, JA Roth, K Chin-Cheong, BL Hug, JE Vogt. (2020). "A comparison of general and disease-specific machine learning models for the prediction of unplanned hospital readmissions." Journal of the American Medical Informatics Association, Volume 28, Issue 4, April 2021. https://academic.oup.com/jamia/article-abstract/28/4/868/6041742
Machine Learning Algorithms Evaluate Immune Response to Novel Mycobacterium tuberculosis Antigens for Diagnosis of Tuberculosis
Published in Frontiers in Cellular and Infection Microbiology, 2021
Identifying optimal antigen-cytokine combinations using novel Mycobacterium tuberculosis antigens and cytokine read-outs.
Recommended citation: NR Meier, TM Sutter, M Jacobsen, THM Ottenhoff, JE Vogt, N Ritz. (2021) "Machine Learning Algorithms Evaluate Immune Response to Novel Mycobacterium tuberculosis Antigens for Diagnosis of Tuberculosis." Front. Cell. Infect. Microbiol.. 10(2020). https://www.frontiersin.org/articles/10.3389/fcimb.2020.594030/full
Generalized Multimodal ELBO
Published in ICLR 2021, 2021
Generalizing previous formulations of scalable multimodal V
Recommended citation: TM Sutter, I Daunhawer, JE Vogt. (2021). "Generalized multimodal ELBO." ICLR 2021. https://arxiv.org/abs/2110.04121
Multimodal Generative Learning on the Mimic-CXR database
Published in MIDL 2021 Short Paper, 2021
Evaluating MoPoE-VAE on the Mimic-Cxr database.
Recommended citation: HJ Klug, TM Sutter, JE Vogt. (2021). "Multimodal Generative Learning on the Mimic-CXR database." MIDL 2021 Short Paper. https://openreview.net/pdf?id=ZVqjoKVbYMl
Multimodal Relational VAE
Published in Neurips 2021 Workshop on Bayesian Deep Learning, 2021
Explicitely modelling and learning the relation between multiple data types.
Recommended citation: TM Sutter, JE Vogt. (2021). "Multimodal Relational VAE." Neurips 2021 Workshop on Bayesian Deep Learning. https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/520271/1/bdl_draft_cameraready_20211203.pdf
On the Limitations of Multimodal VAEs
Published in ICLR 2022, 2022
We show limitations of current scalable, multimodal VAEs.
Recommended citation: I Daunhawer, TM Suttter, K Chin-Cheong, E Palumbo, JE Vogt. (2022). "On the limitations of multimodal VAEs." ICLR 2022. https://arxiv.org/abs/2110.04121
M(otion)-mode Based Prediction of Cardiac Function on Echocardiograms
Published in Medical Imaging meets NeurIPS Workshop 2022, 2022
Artificial (M)otion-mode based predicting of left ventricular ejection fraction to diagnose cardiomyopathy.
Recommended citation: TM Sutter, S Balzer, E Ozkan, JE Vogt. (2022) "M(otion)-mode Based Prediction of Cardiac Function on Echocardiograms." Medical Imaging meets NeurIPS Workshop 2022. https://www.research-collection.ethz.ch/handle/20.500.11850/588779
Learning Group Importance using the Differentiable Hypergeometric Distribution
Published in ICLR 2023 (Spotlight Presentation), 2023
We propose a differentiable formulation for the multivariate hypergeometric distribution.
Recommended citation: TM Sutter, L Manduchi, A Ryser, JE Vogt. (2023). "Learning Group Importance using the Differentiable Hypergeometric Distribution" ICLR 2023. https://arxiv.org/abs/2203.01629
M(otion)-mode Based Prediction of Ejection Fraction using Echocardiograms
Published in GCPR, 2023
Artificial (M)otion-mode based predicting of left ventricular ejection fraction to diagnose cardiomyopathy.
Recommended citation: E Ozkan, TM Sutter, Y Hu, S Balzer, JE Vogt. (2023) "M(otion)-mode Based Prediction of Ejection Fraction using Echocardiograms." GCPR 2023. https://www.dagm-gcpr.de/fileadmin/dagm-gcpr/pictures/2023_Heidelberg/Paper_MainTrack/053.pdf
Differentiable Random Partition Models
Published in NeurIPS, 2023
We propose a differentiable two-stage formulation for random partition models that allows gradient-based optimization of the distribution parameters.
Recommended citation: TM Sutter, A Ryser, J Liebeskind, JE Vogt. (2023) "Differentiable Random Partition Models." NeurIPS 2023. https://arxiv.org/abs/2305.16841
Anomaly Detection by Context Contrasting
Published in arxiv, 2024
We propose a novel approach to anomaly detection using contrastive approaches to learning representations of normal samples.
Recommended citation: A Ryser, TM Sutter, A Marx, JE Vogt (2024) "Anomaly Detection by Context Contrasting." arxiv. https://arxiv.org/abs/2405.18848
Unity by Diversity: Improved Representation Learning in Multimodal VAEs
Published in arxiv, 2024
We propose a novel multimodal VAE that enables multimodal capabilities by only soft-sharing of information between modalities.
Recommended citation: TM Sutter, Y Meng, A Agostini, D Chopard, N Fortin, JE Vogt, B Shahbaba, S Mandt (2024) "Unity by Diversity: Improved Representation Learning in Multimodal VAEs." arxiv. https://arxiv.org/abs/2403.05300
talks
Multimodal and Scalable VAEs
Published:
My colleague, Imant Daunhawer, and I gave a talk at the nextgen_ai workshop at the university of Freiburg.
Maschinelles Lernen und künstliche Intelligenz: Eine Einführung
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CSNOW is a network for women in computer science. Every year, there is a Schnupperstudium where different fields of computer science are introduced to female hiigh school students. The Schnupperstudium consists of talks and workshops and wants to motivate more high school students to start studying computer science.
Generative AI for Good: What are the savior applications of generative AI and what data needs do they have?
Published:
I was invited to be part of the panel about generative ai for good at the data-centric machine learning workshop at ICLR 2024. It was a very interesting discussion with lots of important take aways.
teaching
Data Science for Medicine
undergraduate course, ETH Zurich, 2022
I served as a teaching assistant for this introductory data science block course for medical students. I TAed for this course in the years 2020, 2021, and 2022.
Grundlagen der Informatik
Undergraduate course, ETH Zurich, Department of Computer Science, 2024
I served as a teaching assistant for this introductory computer science course. I TAed for this course in the years 2020 and 2021.