default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDJulia E. Vogt
Person information
- affiliation: ETH Zurich, Switzerland
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[j6]Hanna Ragnarsdóttir, Ece Ozkan
, Holger Michel, Kieran Chin-Cheong, Laura Manduchi, Sven Wellmann, Julia E. Vogt:
Deep Learning Based Prediction of Pulmonary Hypertension in Newborns Using Echocardiograms. Int. J. Comput. Vis. 132(7): 2567-2584 (2024)- [j5]Ricards Marcinkevics, Patricia Reis Wolfertstetter, Ugne Klimiene, Kieran Chin-Cheong, Alyssia Paschke, Julia Zerres, Markus Denzinger, David Niederberger, Sven Wellmann, Ece Ozkan, Christian Knorr, Julia E. Vogt:
Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis. Medical Image Anal. 91: 103042 (2024) - [c35]Kacper Sokol, Julia E. Vogt:
What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks. CHI Extended Abstracts 2024: 370:1-370:8 - [c34]Mike Laszkiewicz, Imant Daunhawer, Julia E. Vogt, Asja Fischer, Johannes Lederer:
Benchmarking the Fairness of Image Upsampling Methods. FAccT 2024: 489-517 - [c33]Emanuele Palumbo, Laura Manduchi, Sonia Laguna, Daphné Chopard, Julia E. Vogt:
Deep Generative Clustering with Multimodal Diffusion Variational Autoencoders. ICLR 2024 - [i42]Ricards Marcinkevics, Sonia Laguna, Moritz Vandenhirtz, Julia E. Vogt:
Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable? CoRR abs/2401.13544 (2024) - [i41]Mike Laszkiewicz, Imant Daunhawer, Julia E. Vogt, Asja Fischer, Johannes Lederer:
Benchmarking the Fairness of Image Upsampling Methods. CoRR abs/2401.13555 (2024) - [i40]Laura Manduchi, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric T. Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van den Broeck, Julia E. Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, Vincent Fortuin:
On the Challenges and Opportunities in Generative AI. CoRR abs/2403.00025 (2024) - [i39]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) - [i38]Kacper Sokol, Julia E. Vogt:
What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks. CoRR abs/2403.12730 (2024) - [i37]Alain Ryser, Thomas M. Sutter, Alexander Marx, Julia E. Vogt:
Anomaly Detection by Context Contrasting. CoRR abs/2405.18848 (2024) - [i36]Moritz Vandenhirtz, Sonia Laguna, Ricards Marcinkevics, Julia E. Vogt:
Stochastic Concept Bottleneck Models. CoRR abs/2406.19272 (2024) - [i35]Moritz Vandenhirtz, Florian Barkmann, Laura Manduchi, Julia E. Vogt, Valentina Boeva:
scTree: Discovering Cellular Hierarchies in the Presence of Batch Effects in scRNA-seq Data. CoRR abs/2406.19300 (2024) - [i34]Jorge da Silva Goncalves, Laura Manduchi, Moritz Vandenhirtz, Julia E. Vogt:
Structured Generations: Using Hierarchical Clusters to guide Diffusion Models. CoRR abs/2407.06124 (2024) - [i33]Emanuele Palumbo, Moritz Vandenhirtz, Alain Ryser, Imant Daunhawer, Julia E. Vogt:
From Logits to Hierarchies: Hierarchical Clustering made Simple. CoRR abs/2410.07858 (2024) - [i32]Jorge da Silva Goncalves, Laura Manduchi, Moritz Vandenhirtz, Julia E. Vogt:
Hierarchical Clustering for Conditional Diffusion in Image Generation. CoRR abs/2410.16910 (2024) - [i31]Alba Carballo-Castro, Sonia Laguna, Moritz Vandenhirtz, Julia E. Vogt:
Exploiting Interpretable Capabilities with Concept-Enhanced Diffusion and Prototype Networks. CoRR abs/2410.18705 (2024) - [i30]Patrik Reizinger, Alice Bizeul, Attila Juhos, Julia E. Vogt, Randall Balestriero, Wieland Brendel, David A. Klindt:
Cross-Entropy Is All You Need To Invert the Data Generating Process. CoRR abs/2410.21869 (2024) - 2023
- [j4]Ricards Marcinkevics, Julia E. Vogt:
Interpretable and explainable machine learning: A methods-centric overview with concrete examples. WIREs Data. Mining. Knowl. Discov. 13(3) (2023) - [c32]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 - [c31]Claudio Fanconi, Moritz Vandenhirtz, Severin Husmann, Julia E. Vogt:
This Reads Like That: Deep Learning for Interpretable Natural Language Processing. EMNLP 2023: 14067-14076 - [c30]Imant Daunhawer, Alice Bizeul, Emanuele Palumbo, Alexander Marx, Julia E. Vogt:
Identifiability Results for Multimodal Contrastive Learning. ICLR 2023 - [c29]Emanuele Palumbo, Imant Daunhawer, Julia E. Vogt:
MMVAE+: Enhancing the Generative Quality of Multimodal VAEs without Compromises. ICLR 2023 - [c28]Yuge Shi, Imant Daunhawer, Julia E. Vogt, Philip H. S. Torr, Amartya Sanyal:
How robust is unsupervised representation learning to distribution shift? ICLR 2023 - [c27]Thomas M. Sutter, Laura Manduchi, Alain Ryser, Julia E. Vogt:
Learning Group Importance using the Differentiable Hypergeometric Distribution. ICLR 2023 - [c26]Zixuan Xiao, Michal Muszynski, Ricards Marcinkevics, Lukas Zimmerli, Adam Daniel Ivankay, Dario Kohlbrenner, Manuel Kuhn, Yves Nordmann, Ulrich Muehlner, Christian F. Clarenbach, Julia E. Vogt, Thomas Brunschwiler:
Breathing New Life into COPD Assessment: Multisensory Home-monitoring for Predicting Severity. ICMI 2023: 84-93 - [c25]Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx:
On the Identifiability and Estimation of Causal Location-Scale Noise Models. ICML 2023: 14316-14332 - [c24]Pawel Czyz, Frederic Grabowski, Julia E. Vogt, Niko Beerenwinkel, Alexander Marx:
Beyond Normal: On the Evaluation of Mutual Information Estimators. NeurIPS 2023 - [c23]Alexander Immer, Emanuele Palumbo, Alexander Marx, Julia E. Vogt:
Effective Bayesian Heteroscedastic Regression with Deep Neural Networks. NeurIPS 2023 - [c22]Laura Manduchi, Moritz Vandenhirtz, Alain Ryser, Julia E. Vogt:
Tree Variational Autoencoders. NeurIPS 2023 - [c21]Thomas M. Sutter, Alain Ryser, Joram Liebeskind, Julia E. Vogt:
Differentiable Random Partition Models. NeurIPS 2023 - [i29]Ricards Marcinkevics, Patricia Reis Wolfertstetter, Ugne Klimiene, Ece Ozkan, Kieran Chin-Cheong, Alyssia Paschke, Julia Zerres, Markus Denzinger, David Niederberger, Sven Wellmann, Christian Knorr, Julia E. Vogt:
Interpretable and Intervenable Ultrasonography-based Machine Learning Models for Pediatric Appendicitis. CoRR abs/2302.14460 (2023) - [i28]Imant Daunhawer, Alice Bizeul, Emanuele Palumbo, Alexander Marx, Julia E. Vogt:
Identifiability Results for Multimodal Contrastive Learning. CoRR abs/2303.09166 (2023) - [i27]Thomas M. Sutter, Alain Ryser, Joram Liebeskind, Julia E. Vogt:
Differentiable Random Partition Models. CoRR abs/2305.16841 (2023) - [i26]Moritz Vandenhirtz, Laura Manduchi, Ricards Marcinkevics, Julia E. Vogt:
Signal Is Harder To Learn Than Bias: Debiasing with Focal Loss. CoRR abs/2305.19671 (2023) - [i25]Kacper Sokol, Julia E. Vogt:
(Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Explainability. CoRR abs/2306.02312 (2023) - [i24]Laura Manduchi, Moritz Vandenhirtz, Alain Ryser, Julia E. Vogt:
Tree Variational Autoencoders. CoRR abs/2306.08984 (2023) - [i23]Pawel Czyz, Frederic Grabowski, Julia E. Vogt, Niko Beerenwinkel, Alexander Marx:
Beyond Normal: On the Evaluation of Mutual Information Estimators. CoRR abs/2306.11078 (2023) - [i22]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) - [i21]Pawel Czyz, Frederic Grabowski, Julia E. Vogt, Niko Beerenwinkel, Alexander Marx:
The Mixtures and the Neural Critics: On the Pointwise Mutual Information Profiles of Fine Distributions. CoRR abs/2310.10240 (2023) - [i20]Claudio Fanconi, Moritz Vandenhirtz, Severin Husmann, Julia E. Vogt:
This Reads Like That: Deep Learning for Interpretable Natural Language Processing. CoRR abs/2310.17010 (2023) - 2022
- [c20]Hanna Ragnarsdóttir, Laura Manduchi, Holger Michel, Fabian Laumer, Sven Wellmann, Ece Ozkan, Julia E. Vogt:
Interpretable Prediction of Pulmonary Hypertension in Newborns Using Echocardiograms. GCPR 2022: 529-542 - [c19]Imant Daunhawer, Thomas M. Sutter, Kieran Chin-Cheong, Emanuele Palumbo, Julia E. Vogt:
On the Limitations of Multimodal VAEs. ICLR 2022 - [c18]Laura Manduchi, Ricards Marcinkevics, Michela Carlotta Massi, Thomas J. Weikert, Alexander Sauter, Verena Gotta, Timothy Müller, Flavio Vasella, Marian C. Neidert, Marc Pfister, Bram Stieltjes, Julia E. Vogt:
A Deep Variational Approach to Clustering Survival Data. ICLR 2022 - [c17]Alain Ryser, Laura Manduchi, Fabian Laumer, Holger Michel, Sven Wellmann, Julia E. Vogt:
Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Models. MLHC 2022: 425-458 - [c16]Ricards Marcinkevics, Ece Ozkan, Julia E. Vogt:
Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods. MLHC 2022: 504-536 - [i19]Thomas M. Sutter, Laura Manduchi, Alain Ryser, Julia E. Vogt:
Continuous Relaxation For The Multivariate Non-Central Hypergeometric Distribution. CoRR abs/2203.01629 (2022) - [i18]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) - [i17]Yuge Shi, Imant Daunhawer, Julia E. Vogt, Philip H. S. Torr, Amartya Sanyal:
How robust are pre-trained models to distribution shift? CoRR abs/2206.08871 (2022) - [i16]Alain Ryser, Laura Manduchi, Fabian Laumer, Holger Michel, Sven Wellmann, Julia E. Vogt:
Interpretable Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Models. CoRR abs/2206.15316 (2022) - [i15]Ricards Marcinkevics, Ece Ozkan, Julia E. Vogt:
Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods. CoRR abs/2208.00781 (2022) - [i14]Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx:
On the Identifiability and Estimation of Causal Location-Scale Noise Models. CoRR abs/2210.09054 (2022) - [i13]Ricards Marcinkevics, Ece Ozkan, Julia E. Vogt:
Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge. CoRR abs/2212.12303 (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-Van 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
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-12-01 01:11 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
