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2020 – today
- 2022
[i14]Thomas M. Sutter, Laura Manduchi, Alain Ryser, Julia E. Vogt:
Continuous Relaxation For The Multivariate Non-Central Hypergeometric Distribution. CoRR abs/2203.01629 (2022)- [i13]Hanna Ragnarsdóttir, Laura Manduchi, Holger Michel, Fabian Laumer, Sven Wellmann, Ece Ozkan, Julia E. Vogt:
Interpretable Prediction of Pulmonary Hypertension in Newborns using Echocardiograms. CoRR abs/2203.13038 (2022) - 2021
- [j3]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) - [c15]Laura Manduchi, Matthias Hüser, Martin Faltys, Julia E. Vogt, Gunnar Rätsch, Vincent Fortuin:
T-DPSOM: an interpretable clustering method for unsupervised learning of patient health states. CHIL 2021: 236-245 - [c14]Juan M. Montoya, Imant Daunhawer, Julia E. Vogt, Marco A. Wiering:
Decoupling State Representation Methods from Reinforcement Learning in Car Racing. ICAART (2) 2021: 752-759 - [c13]Alexander H. Hatteland, Ricards Marcinkevics, Renaud Marquis, Thomas Frick, Ilona Hubbard, Julia E. Vogt, Thomas Brunschwiler, Philippe Ryvlin:
Exploring Relationships between Cerebral and Peripheral Biosignals with Neural Networks. ICDH 2021: 103-113 - [c12]Ricards Marcinkevics, Julia E. Vogt:
Interpretable Models for Granger Causality Using Self-explaining Neural Networks. ICLR 2021 - [c11]Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt:
Generalized Multimodal ELBO. ICLR 2021 - [c10]Pedro Roig Aparicio, Ricards Marcinkevics, Patricia Reis Wolfertstetter, Sven Wellmann, Christian Knorr, Julia E. Vogt:
Learning Medical Risk Scores for Pediatric Appendicitis. ICMLA 2021: 1507-1512 - [c9]Laura Manduchi, Kieran Chin-Cheong, Holger Michel, Sven Wellmann, Julia E. Vogt:
Deep Conditional Gaussian Mixture Model for Constrained Clustering. NeurIPS 2021: 11303-11314 - [i12]Ricards Marcinkevics, Julia E. Vogt:
Interpretable Models for Granger Causality Using Self-explaining Neural Networks. CoRR abs/2101.07600 (2021) - [i11]Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt:
Generalized Multimodal ELBO. CoRR abs/2105.02470 (2021) - [i10]Laura Manduchi, Ricards Marcinkevics, Michela Carlotta Massi, Verena Gotta, Timothy Müller, Flavio Vasella, Marian C. Neidert, Marc Pfister, Julia E. Vogt:
A Deep Variational Approach to Clustering Survival Data. CoRR abs/2106.05763 (2021) - [i9]Laura Manduchi, Kieran Chin-Cheong, Holger Michel, Sven Wellmann, Julia E. Vogt:
Deep Conditional Gaussian Mixture Model for Constrained Clustering. CoRR abs/2106.06385 (2021) - [i8]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
- [c8]Varaha Karthik Pattisapu, Imant Daunhawer, Thomas J. Weikert, Alexander Sauter, Bram Stieltjes, Julia E. Vogt:
PET-Guided Attention Network for Segmentation of Lung Tumors from PET/CT Images. GCPR 2020: 445-458 - [c7]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 - [c6]Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt:
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence. NeurIPS 2020 - [i7]Kieran Chin-Cheong, Thomas M. Sutter, Julia E. Vogt:
Generation of Differentially Private Heterogeneous Electronic Health Records. CoRR abs/2006.03423 (2020) - [i6]Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt:
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence. CoRR abs/2006.08242 (2020) - [i5]Ricards Marcinkevics, Julia E. Vogt:
Interpretability and Explainability: A Machine Learning Zoo Mini-tour. CoRR abs/2012.01805 (2020)
2010 – 2019
- 2019
- [c5]Sandhya Prabhakaran, Julia E. Vogt:
Bayesian Clustering for HIV1 Protease Inhibitor Contact Maps. AIME 2019: 281-285 - [i4]Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt:
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence. ViGIL@NeurIPS 2019 - [i3]Stefan G. Stark, Stephanie L. Hyland, Melanie Fernandes Pradier, Kjong Lehmann, Andreas Wicki, Fernando Pérez-Cruz, Julia E. Vogt, Gunnar Rätsch:
Unsupervised Extraction of Phenotypes from Cancer Clinical Notes for Association Studies. CoRR abs/1904.12973 (2019) - 2015
- [j2]Julia E. Vogt, Marius Kloft, Stefan Stark, Sudhir Raman, Sandhya Prabhakaran, Volker Roth, Gunnar Rätsch:
Probabilistic clustering of time-evolving distance data. Mach. Learn. 100(2-3): 635-654 (2015) - [j1]Julia E. Vogt:
Unsupervised Structure Detection in Biomedical Data. IEEE ACM Trans. Comput. Biol. Bioinform. 12(4): 753-760 (2015) - [i2]Julia E. Vogt, Marius Kloft, Stefan Stark, Sudhir Raman, Sandhya Prabhakaran, Volker Roth, Gunnar Rätsch:
Probabilistic Clustering of Time-Evolving Distance Data. CoRR abs/1504.03701 (2015) - 2013
- [p1]Volker Roth, Thomas J. Fuchs, Julia E. Vogt, Sandhya Prabhakaran, Joachim M. Buhmann:
Structure Preserving Embedding of Dissimilarity Data. Similarity-Based Pattern Analysis and Recognition 2013: 157-177 - 2012
- [c4]Sandhya Prabhakaran, Sudhir Raman, Julia E. Vogt, Volker Roth:
Automatic Model Selection in Archetype Analysis. DAGM/OAGM Symposium 2012: 458-467 - [c3]Julia E. Vogt, Volker Roth:
A Complete Analysis of the l_1, p Group-Lasso. ICML 2012 - [i1]Julia E. Vogt, Volker Roth:
A Complete Analysis of the l_1,p Group-Lasso. CoRR abs/1206.4632 (2012) - 2010
- [c2]Julia E. Vogt, Volker Roth:
The Group-Lasso: l1, INFINITY Regularization versus l1, 2 Regularization. DAGM-Symposium 2010: 252-261 - [c1]Julia E. Vogt, Sandhya Prabhakaran, Thomas J. Fuchs, Volker Roth:
The Translation-invariant Wishart-Dirichlet Process for Clustering Distance Data. ICML 2010: 1111-1118
Coauthor Index
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