Julia E. Vogt

132 papers A* 26B 2C 1Journal 81Unranked 21
YearRankTypeTitle / Venue / Authors
2026 J jnl
Dual-Phase Cross-Modal Contrastive Learning for CMR-Guided ECG Representations for Cardiovascular Disease Assessment.
CoRR
Laura Alvarez-Florez, Angel Bujalance-Gomez, Femke Raijmakers, Samuel Ruipérez-Campillo, Maarten Z. Kolk, Jesse Wiers, Julia E. Vogt, Erik J. Bekkers, Ivana Isgum, Fleur V. Y. Tjong
2026 J jnl
Foundation Model for Cardiac Time Series via Masked Latent Attention.
CoRR
Moritz Vandenhirtz, Samuel Ruipérez-Campillo, Simon Böhi, Sonia Laguna, Irene Cannistraci, Andrea Agostini, Ece Ozkan, Thomas M. Sutter, Julia E. Vogt
2026 J jnl
Reducing diverse sources of noise in ventricular electrical signals using variational autoencoders.
Expert Syst. Appl.
Samuel Ruipérez-Campillo, Alain Ryser, Thomas M. Sutter, Brototo Deb, Ruibin Feng, Prasanth Ganesan, Kelly A. Brennan, Albert J. Rogers, Maarten Z. Kolk, Fleur V. Y. Tjong, Sanjiv M. Narayan, Julia E. Vogt
2026 J jnl
Reference-Guided Machine Unlearning.
CoRR
Jonas Mirlach, Sonia Laguna, Julia E. Vogt
2026 J jnl
Rethinking Machine Unlearning: Models Designed to Forget via Key Deletion.
CoRR
Sonia Laguna, Jorge da Silva Goncalves, Moritz Vandenhirtz, Alain Ryser, Irene Cannistraci, Julia E. Vogt
2026 — conf
Steering Generative Models for Accessibility: EasyRead Image Generation.
CHI Extended Abstracts
Nicolas Dickenmann, Yanis Merzouki, Sonia Laguna, Thy Nowak-Tran, Emanuele Palumbo, Julia E. Vogt, Gerda Binder
2026 J jnl
Steering Generative Models for Accessibility: EasyRead Image Generation.
CoRR
Nicolas Dickenmann, Yanis Merzouki, Sonia Laguna, Thy Nowak-Tran, Emanuele Palumbo, Julia E. Vogt, Gerda Binder
2026 J jnl
Structure is Supervision: Multiview Masked Autoencoders for Radiology.
Trans. Mach. Learn. Res.
Sonia Laguna, Andrea Agostini, Alain Ryser, Samuel Ruipérez-Campillo, Irene Cannistraci, Moritz Vandenhirtz, Stephan Mandt, Nicolas Deperrois, Farhad Nooralahzadeh, Michael Krauthammer, Thomas M. Sutter, Julia E. Vogt
2025 J jnl
A Denoising VAE for Intracardiac Time Series in Ischemic Cardiomyopathy.
CoRR
Samuel Ruipérez-Campillo, Alain Ryser, Thomas M. Sutter, Ruibin Feng, Prasanth Ganesan, Brototo Deb, Kelly A. Brennan, Maxime Pedron, Albert J. Rogers, Maarten Z. Kolk, Fleur V. Y. Tjong, Sanjiv M. Narayan, Julia E. Vogt
2025 J jnl
AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation.
J. Am. Medical Informatics Assoc.
Shalmali Joshi, Iñigo Urteaga, Wouter A. C. van Amsterdam, George Hripcsak, Pierre A. Elias, Benjamin R. C. Amor, Noémie Elhadad, James C. Fackler, Mark P. Sendak, Jenna Wiens, Kaivalya Deshpande, Yoav Wald, Madalina Fiterau, Zachary C. Lipton, Daniel Malinsky, Madhur Nayan, Hongseok Namkoong, Soojin Park, Julia E. Vogt, Rajesh Ranganath
2025 J jnl
Artificial Intelligence Should Genuinely Support Clinical Reasoning and Decision Making To Bridge the Translational Gap.
CoRR
Kacper Sokol, James C. Fackler, Julia E. Vogt
2025 J jnl
Artificial intelligence should genuinely support clinical reasoning and decision making to bridge the translational gap.
npj Digit. Medicine
Kacper Sokol, James C. Fackler, Julia E. Vogt
2025 J jnl
Beyond Glucose-Only Assessment: Advancing Nocturnal Hypoglycemia Prediction in Children with Type 1 Diabetes.
CoRR
Marco Voegeli, Sonia Laguna, Heike Leutheuser, Marc Pfister, Marie-Anne Burckhardt, Julia E. Vogt
2025 A* conf
Cross-Entropy Is All You Need To Invert the Data Generating Process.
ICLR
Patrik Reizinger, Alice Bizeul, Attila Juhos, Julia E. Vogt, Randall Balestriero, Wieland Brendel, David A. Klindt
2025 J jnl
Enhancing Radiology Report Generation and Visual Grounding using Reinforcement Learning.
CoRR
Benjamin Gundersen, Nicolas Deperrois, Samuel Ruipérez-Campillo, Thomas M. Sutter, Julia E. Vogt, Michael Moor, Farhad Nooralahzadeh, Michael Krauthammer
2025 A* conf
From Logits to Hierarchies: Hierarchical Clustering made Simple.
ICML
Emanuele Palumbo, Moritz Vandenhirtz, Alain Ryser, Imant Daunhawer, Julia E. Vogt
2025 J jnl
From Pixels to Components: Eigenvector Masking for Visual Representation Learning.
CoRR
Alice Bizeul, Thomas M. Sutter, Alain Ryser, Bernhard Schölkopf, Julius von Kügelgen, Julia E. Vogt
2025 A* conf
From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection.
ICML
Moritz Vandenhirtz, Julia E. Vogt
2025 J jnl
From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection.
CoRR
Moritz Vandenhirtz, Julia E. Vogt
2025 J jnl
From Research to Certification with Data-Driven Medical Decision Support Systems (Dagstuhl Seminar 25052).
Dagstuhl Reports
Raúl Santos-Rodríguez, Kacper Sokol, Julia E. Vogt, Sven Wellmann
2025 J jnl
From Slices to Structures: Unsupervised 3D Reconstruction of Female Pelvic Anatomy from Freehand Transvaginal Ultrasound.
CoRR
Max Krähenmann, Sergio Tascon-Morales, Fabian Laumer, Julia E. Vogt, Ece Ozkan
2025 J jnl
Interpretable Diffusion Models with B-cos Networks.
CoRR
Nicola Bernold, Moritz Vandenhirtz, Alice Bizeul, Julia E. Vogt
2025 J jnl
Interpretable Reward Modeling with Active Concept Bottlenecks.
CoRR
Sonia Laguna, Katarzyna Kobalczyk, Julia E. Vogt, Mihaela van der Schaar
2025 J jnl
Leveraging the Structure of Medical Data for Improved Representation Learning.
CoRR
Andrea Agostini, Sonia Laguna, Alain Ryser, Samuel Ruipérez-Campillo, Moritz Vandenhirtz, Nicolas Deperrois, Farhad Nooralahzadeh, Michael Krauthammer, Thomas M. Sutter, Julia E. Vogt
2025 J jnl
Measuring Leakage in Concept-Based Methods: An Information Theoretic Approach.
CoRR
Mikael Makonnen, Moritz Vandenhirtz, Sonia Laguna, Julia E. Vogt
2025 J jnl
On the Challenges and Opportunities in Generative AI.
Trans. Mach. Learn. Res.
Laura Manduchi, Clara Meister, 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
2025 J jnl
On the Properties and Estimation of Pointwise Mutual Information Profiles.
Trans. Mach. Learn. Res.
Pawel Czyz, Frederic Grabowski, Julia E. Vogt, Niko Beerenwinkel, Alexander Marx
2025 — conf
PhenoRAG: Retrieval-Augmented Generation for Efficient Zero-Shot Phenotype Identification in Clinical Reports.
MLHC
Marc Berndt, Andrea Agostini, Beatrice Stocker, Maria Padrutt, Silvio Daniel Brugger, D Sean Froese, Daphné Chopard, Julia E. Vogt
2025 J jnl
Post-hoc Stochastic Concept Bottleneck Models.
CoRR
Wiktor Jan Hoffmann, Sonia Laguna, Moritz Vandenhirtz, Emanuele Palumbo, Julia E. Vogt
2025 J jnl
Predicting Pulmonary Hypertension in Newborns: A Multi-view VAE Approach.
CoRR
Lucas Erlacher, Samuel Ruipérez-Campillo, Holger Michel, Sven Wellmann, Thomas M. Sutter, Ece Ozkan, Julia E. Vogt
2025 J jnl
RadVLM: A Multitask Conversational Vision-Language Model for Radiology.
CoRR
Nicolas Deperrois, Hidetoshi Matsuo, Samuel Ruipérez-Campillo, Moritz Vandenhirtz, Sonia Laguna, Alain Ryser, Koji Fujimoto, Mizuho Nishio, Thomas M. Sutter, Julia E. Vogt, Jonas Kluckert, Thomas Frauenfelder, Christian Blüthgen, Farhad Nooralahzadeh, Michael Krauthammer
2025 — conf
Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology.
MICCAI (6)
Boqi Chen, Cédric Vincent-Cuaz, Lydia A. Schoenpflug, Manuel Madeira, Lisa Fournier, Vaishnavi Subramanian, Sonali Andani, Samuel Ruipérez-Campillo, Julia E. Vogt, Raphaëlle Luisier, Dorina Thanou, Viktor H. Koelzer, Pascal Frossard, Gabriele Campanella, Gunnar Rätsch
2025 J jnl
Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology.
CoRR
Boqi Chen, Cédric Vincent-Cuaz, Lydia A. Schoenpflug, Manuel Madeira, Lisa Fournier, Vaishnavi Subramanian, Sonali Andani, Samuel Ruipérez-Campillo, Julia E. Vogt, Raphaëlle Luisier, Dorina Thanou, Viktor H. Koelzer, Pascal Frossard, Gabriele Campanella, Gunnar Rätsch
2025 J jnl
Structure is Supervision: Multiview Masked Autoencoders for Radiology.
CoRR
Sonia Laguna, Andrea Agostini, Alain Ryser, Samuel Ruipérez-Campillo, Irene Cannistraci, Moritz Vandenhirtz, Stephan Mandt, Nicolas Deperrois, Farhad Nooralahzadeh, Michael Krauthammer, Thomas M. Sutter, Julia E. Vogt
2025 J jnl
Temporal Representation Learning for Real-Time Ultrasound Analysis.
CoRR
Yves Stebler, Thomas M. Sutter, Ece Ozkan, Julia E. Vogt
2025 — conf
Towards Scalable Newborn Screening: Automated General Movement Assessment in Uncontrolled Settings.
MLHC
Daphné Chopard, Sonia Laguna, Kieran Chin-Cheong, Annika Dietz, Anna Badura, Sven Wellmann, Julia E. Vogt
2025 — conf
TreeDiffusion: Hierarchical Generative Clustering for Conditional Diffusion.
ECML/PKDD (1)
Jorge da Silva Goncalves, Laura Manduchi, Moritz Vandenhirtz, Julia E. Vogt
2025 J jnl
Two Is Better Than One: Aligned Representation Pairs for Anomaly Detection.
Trans. Mach. Learn. Res.
Alain Ryser, Thomas M. Sutter, Alexander Marx, Julia E. Vogt
2025 J jnl
You Only Train Once: Differentiable Subset Selection for Omics Data.
CoRR
Daphné Chopard, Jorge da Silva Goncalves, Irene Cannistraci, Thomas M. Sutter, Julia E. Vogt
2025 A* conf
scSSL-Bench: Benchmarking Self-Supervised Learning for Single-Cell Data.
ICML
Olga Ovcharenko, Florian Barkmann, Philip Toma, Imant Daunhawer, Julia E. Vogt, Sebastian Schelter, Valentina Boeva
2025 J jnl
scSSL-Bench: Benchmarking Self-Supervised Learning for Single-Cell Data.
CoRR
Olga Ovcharenko, Florian Barkmann, Philip Toma, Imant Daunhawer, Julia E. Vogt, Sebastian Schelter, Valentina Boeva
2024 J jnl
Anomaly Detection by Context Contrasting.
CoRR
Alain Ryser, Thomas M. Sutter, Alexander Marx, Julia E. Vogt
2024 J jnl
Automatic Classification of General Movements in Newborns.
CoRR
Daphné Chopard, Sonia Laguna, Kieran Chin-Cheong, Annika Dietz, Anna Badura, Sven Wellmann, Julia E. Vogt
2024 — conf
Benchmarking the Fairness of Image Upsampling Methods.
FAccT
Mike Laszkiewicz, Imant Daunhawer, Julia E. Vogt, Asja Fischer, Johannes Lederer
2024 J jnl
Benchmarking the Fairness of Image Upsampling Methods.
CoRR
Mike Laszkiewicz, Imant Daunhawer, Julia E. Vogt, Asja Fischer, Johannes Lederer
2024 A* conf
Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable?
NeurIPS
Sonia Laguna, Ricards Marcinkevics, Moritz Vandenhirtz, Julia E. Vogt
2024 J jnl
Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable?
CoRR
Ricards Marcinkevics, Sonia Laguna, Moritz Vandenhirtz, Julia E. Vogt
2024 — conf
Can Generative AI Learn Physiological Waveform Morphologies? A Study on Denoising Intracardiac Signals in Ischemic Cardiomyopathy.
EMBC
Samuel Ruipérez-Campillo, Alain Ryser, Thomas M. Sutter, Ruibin Feng, Prasanth Ganesan, Brototo Deb, Kelly A. Brennan, Maarten Z. Kolk, Fleur V. Y. Tjong, Albert J. Rogers, Sanjiv M. Narayan, Julia E. Vogt
2024 J jnl
Cross-Entropy Is All You Need To Invert the Data Generating Process.
CoRR
Patrik Reizinger, Alice Bizeul, Attila Juhos, Julia E. Vogt, Randall Balestriero, Wieland Brendel, David A. Klindt
2024 A* conf
Deep Generative Clustering with Multimodal Diffusion Variational Autoencoders.
ICLR
Emanuele Palumbo, Laura Manduchi, Sonia Laguna, Daphné Chopard, Julia E. Vogt
2024 J jnl
Deep Learning Based Prediction of Pulmonary Hypertension in Newborns Using Echocardiograms.
Int. J. Comput. Vis.
Hanna Ragnarsdóttir, Ece Ozkan, Holger Michel, Kieran Chin-Cheong, Laura Manduchi, Sven Wellmann, Julia E. Vogt
2024 J jnl
Exploiting Interpretable Capabilities with Concept-Enhanced Diffusion and Prototype Networks.
CoRR
Alba Carballo-Castro, Sonia Laguna, Moritz Vandenhirtz, Julia E. Vogt
2024 J jnl
From Logits to Hierarchies: Hierarchical Clustering made Simple.
CoRR
Emanuele Palumbo, Moritz Vandenhirtz, Alain Ryser, Imant Daunhawer, Julia E. Vogt
2024 J jnl
Hierarchical Clustering for Conditional Diffusion in Image Generation.
CoRR
Jorge da Silva Goncalves, Laura Manduchi, Moritz Vandenhirtz, Julia E. Vogt
2024 J jnl
Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis.
Medical Image Anal.
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
2024 J jnl
On the Challenges and Opportunities in Generative AI.
CoRR
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
2024 A* conf
Stochastic Concept Bottleneck Models.
NeurIPS
Moritz Vandenhirtz, Sonia Laguna, Ricards Marcinkevics, Julia E. Vogt
2024 J jnl
Stochastic Concept Bottleneck Models.
CoRR
Moritz Vandenhirtz, Sonia Laguna, Ricards Marcinkevics, Julia E. Vogt
2024 J jnl
Structured Generations: Using Hierarchical Clusters to guide Diffusion Models.
CoRR
Jorge da Silva Goncalves, Laura Manduchi, Moritz Vandenhirtz, Julia E. Vogt
2024 — conf
Transfer Learning for Pediatric Glucose Forecasting.
ML4H@NeurIPS
Alain Ryser, Chuhao Feng, Tobias Scheithauer, Marc Pfister, Marie-Anne Burckhardt, Sara Bachmann, Alexander Marx, Julia E. Vogt
2024 A* conf
Unity by Diversity: Improved Representation Learning for Multimodal VAEs.
NeurIPS
Thomas M. Sutter, Yang Meng, Andrea Agostini, Daphné Chopard, Norbert Fortin, Julia E. Vogt, Babak Shahbaba, Stephan Mandt
2024 J jnl
Unity by Diversity: Improved Representation Learning in Multimodal VAEs.
CoRR
Thomas M. Sutter, Yang Meng, Norbert Fortin, Julia E. Vogt, Stephan Mandt
2024 J jnl
Weakly-Supervised Multimodal Learning on MIMIC-CXR.
CoRR
Andrea Agostini, Daphné Chopard, Yang Meng, Norbert Fortin, Babak Shahbaba, Stephan Mandt, Thomas M. Sutter, Julia E. Vogt
2024 — conf
What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks.
CHI Extended Abstracts
Kacper Sokol, Julia E. Vogt
2024 J jnl
What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks.
CoRR
Kacper Sokol, Julia E. Vogt
2024 J jnl
scTree: Discovering Cellular Hierarchies in the Presence of Batch Effects in scRNA-seq Data.
CoRR
Moritz Vandenhirtz, Florian Barkmann, Laura Manduchi, Julia E. Vogt, Valentina Boeva
2023 J jnl
(Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Explainability.
CoRR
Kacper Sokol, Julia E. Vogt
2023 A* conf
Beyond Normal: On the Evaluation of Mutual Information Estimators.
NeurIPS
Pawel Czyz, Frederic Grabowski, Julia E. Vogt, Niko Beerenwinkel, Alexander Marx
2023 J jnl
Beyond Normal: On the Evaluation of Mutual Information Estimators.
CoRR
Pawel Czyz, Frederic Grabowski, Julia E. Vogt, Niko Beerenwinkel, Alexander Marx
2023 B conf
Breathing New Life into COPD Assessment: Multisensory Home-monitoring for Predicting Severity.
ICMI
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
2023 A* conf
Differentiable Random Partition Models.
NeurIPS
Thomas M. Sutter, Alain Ryser, Joram Liebeskind, Julia E. Vogt
2023 J jnl
Differentiable Random Partition Models.
CoRR
Thomas M. Sutter, Alain Ryser, Joram Liebeskind, Julia E. Vogt
2023 A* conf
Effective Bayesian Heteroscedastic Regression with Deep Neural Networks.
NeurIPS
Alexander Immer, Emanuele Palumbo, Alexander Marx, Julia E. Vogt
2023 A* conf
How robust is unsupervised representation learning to distribution shift?
ICLR
Yuge Shi, Imant Daunhawer, Julia E. Vogt, Philip H. S. Torr, Amartya Sanyal
2023 A* conf
Identifiability Results for Multimodal Contrastive Learning.
ICLR
Imant Daunhawer, Alice Bizeul, Emanuele Palumbo, Alexander Marx, Julia E. Vogt
2023 J jnl
Identifiability Results for Multimodal Contrastive Learning.
CoRR
Imant Daunhawer, Alice Bizeul, Emanuele Palumbo, Alexander Marx, Julia E. Vogt
2023 J jnl
Interpretable and Intervenable Ultrasonography-based Machine Learning Models for Pediatric Appendicitis.
CoRR
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
2023 J jnl
Interpretable and explainable machine learning: A methods-centric overview with concrete examples.
WIREs Data. Mining. Knowl. Discov.
Ricards Marcinkevics, Julia E. Vogt
2023 A* conf
Learning Group Importance using the Differentiable Hypergeometric Distribution.
ICLR
Thomas M. Sutter, Laura Manduchi, Alain Ryser, Julia E. Vogt
2023 — conf
M(otion)-Mode Based Prediction of Ejection Fraction Using Echocardiograms.
DAGM
Ece Ozkan, Thomas M. Sutter, Yurong Hu, Sebastian Balzer, Julia E. Vogt
2023 J jnl
M(otion)-mode Based Prediction of Ejection Fraction using Echocardiograms.
CoRR
Ece Ozkan, Thomas M. Sutter, Yurong Hu, Sebastian Balzer, Julia E. Vogt
2023 A* conf
MMVAE+: Enhancing the Generative Quality of Multimodal VAEs without Compromises.
ICLR
Emanuele Palumbo, Imant Daunhawer, Julia E. Vogt
2023 A* conf
On the Identifiability and Estimation of Causal Location-Scale Noise Models.
ICML
Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx
2023 J jnl
Signal Is Harder To Learn Than Bias: Debiasing with Focal Loss.
CoRR
Moritz Vandenhirtz, Laura Manduchi, Ricards Marcinkevics, Julia E. Vogt
2023 J jnl
The Mixtures and the Neural Critics: On the Pointwise Mutual Information Profiles of Fine Distributions.
CoRR
Pawel Czyz, Frederic Grabowski, Julia E. Vogt, Niko Beerenwinkel, Alexander Marx
2023 A* conf
This Reads Like That: Deep Learning for Interpretable Natural Language Processing.
EMNLP
Claudio Fanconi, Moritz Vandenhirtz, Severin Husmann, Julia E. Vogt
2023 J jnl
This Reads Like That: Deep Learning for Interpretable Natural Language Processing.
CoRR
Claudio Fanconi, Moritz Vandenhirtz, Severin Husmann, Julia E. Vogt
2023 A* conf
Tree Variational Autoencoders.
NeurIPS
Laura Manduchi, Moritz Vandenhirtz, Alain Ryser, Julia E. Vogt
2023 J jnl
Tree Variational Autoencoders.
CoRR
Laura Manduchi, Moritz Vandenhirtz, Alain Ryser, Julia E. Vogt
2022 A* conf
A Deep Variational Approach to Clustering Survival Data.
ICLR
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
2022 — conf
Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Models.
MLHC
Alain Ryser, Laura Manduchi, Fabian Laumer, Holger Michel, Sven Wellmann, Julia E. Vogt
2022 J jnl
Continuous Relaxation For The Multivariate Non-Central Hypergeometric Distribution.
CoRR
Thomas M. Sutter, Laura Manduchi, Alain Ryser, Julia E. Vogt
2022 — conf
Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods.
MLHC
Ricards Marcinkevics, Ece Ozkan, Julia E. Vogt
2022 J jnl
Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods.
CoRR
Ricards Marcinkevics, Ece Ozkan, Julia E. Vogt
2022 J jnl
How robust are pre-trained models to distribution shift?
CoRR
Yuge Shi, Imant Daunhawer, Julia E. Vogt, Philip H. S. Torr, Amartya Sanyal
2022 J jnl
Interpretable Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Models.
CoRR
Alain Ryser, Laura Manduchi, Fabian Laumer, Holger Michel, Sven Wellmann, Julia E. Vogt
2022 — conf
Interpretable Prediction of Pulmonary Hypertension in Newborns Using Echocardiograms.
GCPR
Hanna Ragnarsdóttir, Laura Manduchi, Holger Michel, Fabian Laumer, Sven Wellmann, Ece Ozkan, Julia E. Vogt
2022 J jnl
Interpretable Prediction of Pulmonary Hypertension in Newborns using Echocardiograms.
CoRR
Hanna Ragnarsdóttir, Laura Manduchi, Holger Michel, Fabian Laumer, Sven Wellmann, Ece Ozkan, Julia E. Vogt
2022 J jnl
Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge.
CoRR
Ricards Marcinkevics, Ece Ozkan, Julia E. Vogt
2022 J jnl
On the Identifiability and Estimation of Causal Location-Scale Noise Models.
CoRR
Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx
2022 A* conf
On the Limitations of Multimodal VAEs.
ICLR
Imant Daunhawer, Thomas M. Sutter, Kieran Chin-Cheong, Emanuele Palumbo, Julia E. Vogt
2021 J jnl
A Deep Variational Approach to Clustering Survival Data.
CoRR
Laura Manduchi, Ricards Marcinkevics, Michela Carlotta Massi, Verena Gotta, Timothy Müller, Flavio Vasella, Marian C. Neidert, Marc Pfister, Julia E. Vogt
2021 J jnl
A comparison of general and disease-specific machine learning models for the prediction of unplanned hospital readmissions.
J. Am. Medical Informatics Assoc.
Thomas M. Sutter, Jan A. Roth, Kieran Chin-Cheong, Balthasar L. Hug, Julia E. Vogt
2021 — conf
Decoupling State Representation Methods from Reinforcement Learning in Car Racing.
ICAART (2)
Juan M. Montoya, Imant Daunhawer, Julia E. Vogt, Marco A. Wiering
2021 A* conf
Deep Conditional Gaussian Mixture Model for Constrained Clustering.
NeurIPS
Laura Manduchi, Kieran Chin-Cheong, Holger Michel, Sven Wellmann, Julia E. Vogt
2021 J jnl
Deep Conditional Gaussian Mixture Model for Constrained Clustering.
CoRR
Laura Manduchi, Kieran Chin-Cheong, Holger Michel, Sven Wellmann, Julia E. Vogt
2021 — conf
Exploring Relationships between Cerebral and Peripheral Biosignals with Neural Networks.
ICDH
Alexander H. Hatteland, Ricards Marcinkevics, Renaud Marquis, Thomas Frick, Ilona Hubbard, Julia E. Vogt, Thomas Brunschwiler, Philippe Ryvlin
2021 A* conf
Generalized Multimodal ELBO.
ICLR
Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt
2021 J jnl
Generalized Multimodal ELBO.
CoRR
Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt
2021 A* conf
Interpretable Models for Granger Causality Using Self-explaining Neural Networks.
ICLR
Ricards Marcinkevics, Julia E. Vogt
2021 J jnl
Interpretable Models for Granger Causality Using Self-explaining Neural Networks.
CoRR
Ricards Marcinkevics, Julia E. Vogt
2021 C conf
Learning Medical Risk Scores for Pediatric Appendicitis.
ICMLA
Pedro Roig Aparicio, Ricards Marcinkevics, Patricia Reis Wolfertstetter, Sven Wellmann, Christian Knorr, Julia E. Vogt
2021 J jnl
On the Limitations of Multimodal VAEs.
CoRR
Imant Daunhawer, Thomas M. Sutter, Kieran Chin-Cheong, Emanuele Palumbo, Julia E. Vogt
2021 — conf
T-DPSOM: an interpretable clustering method for unsupervised learning of patient health states.
CHIL
Laura Manduchi, Matthias Hüser, Martin Faltys, Julia E. Vogt, Gunnar Rätsch, Vincent Fortuin
2020 J jnl
Generation of Differentially Private Heterogeneous Electronic Health Records.
CoRR
Kieran Chin-Cheong, Thomas M. Sutter, Julia E. Vogt
2020 J jnl
Interpretability and Explainability: A Machine Learning Zoo Mini-tour.
CoRR
Ricards Marcinkevics, Julia E. Vogt
2020 A* conf
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence.
NeurIPS
Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt
2020 J jnl
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence.
CoRR
Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt
2020 — conf
PET-Guided Attention Network for Segmentation of Lung Tumors from PET/CT Images.
GCPR
Varaha Karthik Pattisapu, Imant Daunhawer, Thomas J. Weikert, Alexander Sauter, Bram Stieltjes, Julia E. Vogt
2020 — conf
Self-supervised Disentanglement of Modality-Specific and Shared Factors Improves Multimodal Generative Models.
GCPR
Imant Daunhawer, Thomas M. Sutter, Ricards Marcinkevics, Julia E. Vogt
2019 B conf
Bayesian Clustering for HIV1 Protease Inhibitor Contact Maps.
AIME
Sandhya Prabhakaran, Julia E. Vogt
2019 — conf
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence.
ViGIL@NeurIPS
Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt
2019 J jnl
Unsupervised Extraction of Phenotypes from Cancer Clinical Notes for Association Studies.
CoRR
Stefan G. Stark, Stephanie L. Hyland, Melanie Fernandes Pradier, Kjong-Van Lehmann, Andreas Wicki, Fernando Pérez-Cruz, Julia E. Vogt, Gunnar Rätsch
2015 J jnl
Probabilistic Clustering of Time-Evolving Distance Data.
CoRR
Julia E. Vogt, Marius Kloft, Stefan Stark, Sudhir Raman, Sandhya Prabhakaran, Volker Roth, Gunnar Rätsch
2015 J jnl
Probabilistic clustering of time-evolving distance data.
Mach. Learn.
Julia E. Vogt, Marius Kloft, Stefan Stark, Sudhir Raman, Sandhya Prabhakaran, Volker Roth, Gunnar Rätsch
2015 J jnl
Unsupervised Structure Detection in Biomedical Data.
IEEE ACM Trans. Comput. Biol. Bioinform.
Julia E. Vogt
2013 — ch.
Structure Preserving Embedding of Dissimilarity Data.
Similarity-Based Pattern Analysis and Recognition
Volker Roth, Thomas J. Fuchs, Julia E. Vogt, Sandhya Prabhakaran, Joachim M. Buhmann
2012 A* conf
A Complete Analysis of the l_1, p Group-Lasso.
ICML
Julia E. Vogt, Volker Roth
2012 J jnl
A Complete Analysis of the l_1,p Group-Lasso
CoRR
Julia E. Vogt, Volker Roth
2012 — conf
Automatic Model Selection in Archetype Analysis.
DAGM/OAGM Symposium
Sandhya Prabhakaran, Sudhir Raman, Julia E. Vogt, Volker Roth
2010 — conf
The Group-Lasso:
DAGM-Symposium
Julia E. Vogt, Volker Roth
2010 A* conf
The Translation-invariant Wishart-Dirichlet Process for Clustering Distance Data.
ICML
Julia E. Vogt, Sandhya Prabhakaran, Thomas J. Fuchs, Volker Roth