Ullrich Köthe

ORCID

Publication Activity (10 Years)

Years Active: 1995-2024
Publications (10 Years): 81

Top Venues

CoRR

GCPR

CVPR

IEEE Trans. Neural Networks Learn. Syst.

Publications

Peter Sorrenson, Felix Draxler, Armand Rousselot, Sander Hummerich, Lea Zimmermann, Ullrich Köthe
Lifting Architectural Constraints of Injective Flows. ICLR (2024)
Peter Lorenz, Mario Fernandez, Jens Müller, Ullrich Köthe
Deciphering the Definition of Adversarial Robustness for post-hoc OOD Detectors. CoRR (2024)
Peter Sorrenson, Daniel Behrend-Uriarte, Christoph Schnörr, Ullrich Köthe
Learning Distances from Data with Normalizing Flows and Score Matching. CoRR (2024)
Michael Götz, Christian Weber, Franciszek Binczyk, Joanna Polanska, Rafal Tarnawski, Barbara Bobek-Billewicz, Ullrich Köthe, Jens Kleesiek, Bram Stieltjes, Klaus H. Maier-Hein
DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images. CoRR (2024)
Felix Draxler, Stefan Wahl, Christoph Schnörr, Ullrich Köthe
On the Universality of Coupling-based Normalizing Flows. CoRR (2024)
Felix Draxler, Peter Sorrenson, Lea Zimmermann, Armand Rousselot, Ullrich Köthe
Free-form Flows: Make Any Architecture a Normalizing Flow. AISTATS (2024)
Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation. CoRR (2024)
Felix Draxler, Peter Sorrenson, Lea Zimmermann, Armand Rousselot, Ullrich Köthe
Free-form Flows: Make Any Architecture a Normalizing Flow. CoRR (2023)
Robert Schmier, Ullrich Köthe, Christoph-Nikolas Straehle
Positive Difference Distribution for Image Outlier Detection using Normalizing Flows and Contrastive Data. Trans. Mach. Learn. Res. 2023 (2023)
Felix Draxler, Lars Kühmichel, Armand Rousselot, Jens Müller, Christoph Schnörr, Ullrich Köthe
On the Convergence Rate of Gaussianization with Random Rotations. CoRR (2023)
Kris K. Dreher, Leonardo Ayala, Melanie Schellenberg, Marco Hübner, Jan-Hinrich Nölke, Tim J. Adler, Silvia Seidlitz, Jan Sellner, Alexander Studier-Fischer, Janek Gröhl, Felix Nickel, Ullrich Köthe, Alexander Seitel, Lena Maier-Hein
Unsupervised Domain Transfer with Conditional Invertible Neural Networks. MICCAI (1) (2023)
Stefan T. Radev, Marvin Schmitt, Valentin Pratz, Umberto Picchini, Ullrich Köthe, Paul-Christian Bürkner
JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models. CoRR (2023)
Jens Müller, Lars Kühmichel, Martin Rohbeck, Stefan T. Radev, Ullrich Köthe
Towards Context-Aware Domain Generalization: Representing Environments with Permutation-Invariant Networks. CoRR (2023)
Peter Sorrenson, Felix Draxler, Armand Rousselot, Sander Hummerich, Ullrich Köthe
Learning Distributions on Manifolds with Free-form Flows. CoRR (2023)
Jens Müller, Stefan T. Radev, Robert Schmier, Felix Draxler, Carsten Rother, Ullrich Köthe
Finding Competence Regions in Domain Generalization. CoRR (2023)
Lasse Elsemüller, Hans Olischläger, Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev
Sensitivity-Aware Amortized Bayesian Inference. CoRR (2023)
Stefan T. Radev, Marvin Schmitt, Lukas Schumacher, Lasse Elsemüller, Valentin Pratz, Yannik Schälte, Ullrich Köthe, Paul-Christian Bürkner
BayesFlow: Amortized Bayesian Workflows With Neural Networks. J. Open Source Softw. 8 (90) (2023)
Stefan T. Radev, Marco D'Alessandro, Ulf K. Mertens, Andreas Voss, Ullrich Köthe, Paul-Christian Bürkner
Amortized Bayesian Model Comparison With Evidential Deep Learning. IEEE Trans. Neural Networks Learn. Syst. 34 (8) (2023)
Tim J. Adler, Jan-Hinrich Nölke, Annika Reinke, Minu Dietlinde Tizabi, Sebastian Gruber, Dasha Trofimova, Lynton Ardizzone, Paul F. Jaeger, Florian Büttner, Ullrich Köthe, Lena Maier-Hein
Application-driven Validation of Posteriors in Inverse Problems. CoRR (2023)
Marvin Schmitt, Valentin Pratz, Ullrich Köthe, Paul-Christian Bürkner, Stefan T. Radev
Consistency Models for Scalable and Fast Simulation-Based Inference. CoRR (2023)
The-Gia Leo Nguyen, Lynton Ardizzone, Ullrich Köthe
Training Invertible Neural Networks as Autoencoders. CoRR (2023)
Stefan T. Radev, Marvin Schmitt, Lukas Schumacher, Lasse Elsemüller, Valentin Pratz, Yannik Schälte, Ullrich Köthe, Paul-Christian Bürkner
BayesFlow: Amortized Bayesian Workflows With Neural Networks. CoRR (2023)
Jens Müller, Stefan T. Radev, Robert Schmier, Felix Draxler, Carsten Rother, Ullrich Köthe
Finding Competence Regions in Domain Generalization. Trans. Mach. Learn. Res. 2023 (2023)
Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks. DAGM (2023)
Kris K. Dreher, Leonardo Ayala, Melanie Schellenberg, Marco Hübner, Jan-Hinrich Nölke, Tim J. Adler, Silvia Seidlitz, Jan Sellner, Alexander Studier-Fischer, Janek Gröhl, Felix Nickel, Ullrich Köthe, Alexander Seitel, Lena Maier-Hein
Unsupervised Domain Transfer with Conditional Invertible Neural Networks. CoRR (2023)
Jonathan Wider, Jakob Kruse, Nils Weitzel, Janica C. Bühler, Ullrich Köthe, Kira Rehfeld
Towards Learned Emulation of Interannual Water Isotopologue Variations in General Circulation Models. CoRR (2023)
Felix Draxler, Lars Kühmichel, Armand Rousselot, Jens Müller, Christoph Schnörr, Ullrich Köthe
On the Convergence Rate of Gaussianization with Random Rotations. ICML (2023)
Marvin Schmitt, Daniel Habermann, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev
Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference. CoRR (2023)
Ullrich Köthe
A Review of Change of Variable Formulas for Generative Modeling. CoRR (2023)
Stefan T. Radev, Marvin Schmitt, Valentin Pratz, Umberto Picchini, Ullrich Köthe, Paul-Christian Bürkner
Jana: Jointly amortized neural approximation of complex Bayesian models. UAI (2023)
Peter Sorrenson, Felix Draxler, Armand Rousselot, Sander Hummerich, Lea Zimmerman, Ullrich Köthe
Maximum Likelihood Training of Autoencoders. CoRR (2023)
Robert Schmier, Ullrich Köthe, Christoph-Nikolas Straehle
Anomaly Detection using Contrastive Normalizing Flows. CoRR (2022)
Felix Draxler, Christoph Schnörr, Ullrich Köthe
Whitening Convergence Rate of Coupling-based Normalizing Flows. CoRR (2022)
Malte Tölle, Ullrich Köthe, Florian André, Benjamin Meder, Sandy Engelhardt
Content-Aware Differential Privacy with Conditional Invertible Neural Networks. DeCaF/FAIR@MICCAI (2022)
Malte Tölle, Ullrich Köthe, Florian André, Benjamin Meder, Sandy Engelhardt
Content-Aware Differential Privacy with Conditional Invertible Neural Networks. CoRR (2022)
Titus Leistner, Radek Mackowiak, Lynton Ardizzone, Ullrich Köthe, Carsten Rother
Towards Multimodal Depth Estimation from Light Fields. CVPR (2022)
Jonas Haldemann, Victor Ksoll, Daniel Walter, Yann Alibert, Ralf S. Klessen, Willy Benz, Ullrich Köthe, Lynton Ardizzone, Carsten Rother
Exoplanet Characterization using Conditional Invertible Neural Networks. CoRR (2022)
Stefan T. Radev, Ulf K. Mertens, Andreas Voss, Lynton Ardizzone, Ullrich Köthe
BayesFlow: Learning Complex Stochastic Models With Invertible Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 33 (4) (2022)
Titus Leistner, Radek Mackowiak, Lynton Ardizzone, Ullrich Köthe, Carsten Rother
Towards Multimodal Depth Estimation from Light Fields. CoRR (2022)
Felix Draxler, Christoph Schnörr, Ullrich Köthe
Whitening Convergence Rate of Coupling-based Normalizing Flows. NeurIPS (2022)
Jens Müller, Robert Schmier, Lynton Ardizzone, Carsten Rother, Ullrich Köthe
Learning Robust Models Using the Principle of Independent Causal Mechanisms. GCPR (2021)
Steffen Wolf, Alberto Bailoni, Constantin Pape, Nasim Rahaman, Anna Kreshuk, Ullrich Köthe, Fred A. Hamprecht
The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning. IEEE Trans. Pattern Anal. Mach. Intell. 43 (10) (2021)
Lynton Ardizzone, Jakob Kruse, Carsten Lüth, Niels Bracher, Carsten Rother, Ullrich Köthe
Conditional Invertible Neural Networks for Diverse Image-to-Image Translation. CoRR (2021)
Radek Mackowiak, Lynton Ardizzone, Ullrich Köthe, Carsten Rother
Generative Classifiers as a Basis for Trustworthy Image Classification. CVPR (2021)
Jakob Kruse, Gianluca Detommaso, Ullrich Köthe, Robert Scheichl
HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference. AAAI (2021)
Jan-Hinrich Nölke, Tim Adler, Janek Gröhl, Thomas Kirchner, Lynton Ardizzone, Carsten Rother, Ullrich Köthe, Lena Maier-Hein
Invertible Neural Networks for Uncertainty Quantification in Photoacoustic Imaging. Bildverarbeitung für die Medizin (2021)
Jakob Kruse, Lynton Ardizzone, Carsten Rother, Ullrich Köthe
Benchmarking Invertible Architectures on Inverse Problems. CoRR (2021)
Stefan T. Radev, Frederik Graw, Simiao Chen, Nico T. Mutters, Vanessa Eichel, Till Bärnighausen, Ullrich Köthe
OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany. PLoS Comput. Biol. 17 (10) (2021)
Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev
BayesFlow can reliably detect Model Misspecification and Posterior Errors in Amortized Bayesian Inference. CoRR (2021)
Jonathan Schwarz, Felix Draxler, Ullrich Köthe, Christoph Schnörr
Riemannian SOS-Polynomial Normalizing Flows. GCPR (2020)
Jan-Hinrich Nölke, Tim Adler, Janek Gröhl, Thomas Kirchner, Lynton Ardizzone, Carsten Rother, Ullrich Köthe, Lena Maier-Hein
Invertible Neural Networks for Uncertainty Quantification in Photoacoustic Imaging. CoRR (2020)
Stefan T. Radev, Frederik Graw, Simiao Chen, Nico T. Mutters, Vanessa Eichel, Till Bärnighausen, Ullrich Köthe
Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks. CoRR (2020)
Radek Mackowiak, Lynton Ardizzone, Ullrich Köthe, Carsten Rother
Generative Classifiers as a Basis for Trustworthy Computer Vision. CoRR (2020)
Felix Draxler, Jonathan Schwarz, Christoph Schnörr, Ullrich Köthe
Characterizing the Role of a Single Coupling Layer in Affine Normalizing Flows. GCPR (2020)
Stefan T. Radev, Ulf K. Mertens, Andreas Voss, Lynton Ardizzone, Ullrich Köthe
BayesFlow: Learning complex stochastic models with invertible neural networks. CoRR (2020)
Stefan T. Radev, Marco D'Alessandro, Paul-Christian Bürkner, Ulf K. Mertens, Andreas Voss, Ullrich Köthe
Amortized Bayesian model comparison with evidential deep learning. CoRR (2020)
Lynton Ardizzone, Radek Mackowiak, Ullrich Köthe, Carsten Rother
Exact Information Bottleneck with Invertible Neural Networks: Getting the Best of Discriminative and Generative Modeling. CoRR (2020)
Lynton Ardizzone, Jakob Kruse, Carsten Lüth, Niels Bracher, Carsten Rother, Ullrich Köthe
Conditional Invertible Neural Networks for Diverse Image-to-Image Translation. GCPR (2020)
Peter Sorrenson, Carsten Rother, Ullrich Köthe
Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN). CoRR (2020)
Jens Müller, Robert Schmier, Lynton Ardizzone, Carsten Rother, Ullrich Köthe
Learning Robust Models Using The Principle of Independent Causal Mechanisms. CoRR (2020)
Darya Trofimova, Tim Adler, Lisa Kausch, Lynton Ardizzone, Klaus H. Maier-Hein, Ullrich Köthe, Carsten Rother, Lena Maier-Hein
Representing Ambiguity in Registration Problems with Conditional Invertible Neural Networks. CoRR (2020)
Lynton Ardizzone, Radek Mackowiak, Carsten Rother, Ullrich Köthe
Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification. NeurIPS (2020)
Peter Sorrenson, Carsten Rother, Ullrich Köthe
Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN). ICLR (2020)
Ricard Durall, Franz-Josef Pfreundt, Ullrich Köthe, Janis Keuper
Object Segmentation using Pixel-wise Adversarial Loss. CoRR (2019)
The-Gia Leo Nguyen, Lynton Ardizzone, Ullrich Köthe
Training Invertible Neural Networks as Autoencoders. GCPR (2019)
Tim J. Adler, Leonardo Ayala, Lynton Ardizzone, Hannes Kenngott, Anant Suraj Vemuri, Beat P. Müller-Stich, Carsten Rother, Ullrich Köthe, Lena Maier-Hein
Out of Distribution Detection for Intra-operative Functional Imaging. UNSURE/CLIP@MICCAI (2019)
Lynton Ardizzone, Carsten Lüth, Jakob Kruse, Carsten Rother, Ullrich Köthe
Guided Image Generation with Conditional Invertible Neural Networks. CoRR (2019)
Tim J. Adler, Lynton Ardizzone, Anant Suraj Vemuri, Leonardo Ayala, Janek Gröhl, Thomas Kirchner, Sebastian J. Wirkert, Jakob Kruse, Carsten Rother, Ullrich Köthe, Lena Maier-Hein
Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks. Int. J. Comput. Assist. Radiol. Surg. 14 (6) (2019)
Ricard Durall, Franz-Josef Pfreundt, Ullrich Köthe, Janis Keuper
Object Segmentation Using Pixel-Wise Adversarial Loss. GCPR (2019)
Steffen Wolf, Alberto Bailoni, Constantin Pape, Nasim Rahaman, Anna Kreshuk, Ullrich Köthe, Fred A. Hamprecht
The Mutex Watershed and its Objective: Efficient, Parameter-Free Image Partitioning. CoRR (2019)
Tim J. Adler, Lynton Ardizzone, Anant Suraj Vemuri, Leonardo Ayala, Janek Gröhl, Thomas Kirchner, Sebastian J. Wirkert, Jakob Kruse, Carsten Rother, Ullrich Köthe, Lena Maier-Hein
Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks. CoRR (2019)
Gianluca Detommaso, Jakob Kruse, Lynton Ardizzone, Carsten Rother, Ullrich Köthe, Robert Scheichl
HINT: Hierarchical Invertible Neural Transport for General and Sequential Bayesian inference. CoRR (2019)
Lynton Ardizzone, Jakob Kruse, Carsten Rother, Ullrich Köthe
Analyzing Inverse Problems with Invertible Neural Networks. ICLR (Poster) (2019)
Tim J. Adler, Leonardo Ayala, Lynton Ardizzone, Hannes Kenngott, Anant Suraj Vemuri, Beat P. Müller-Stich, Carsten Rother, Ullrich Köthe, Lena Maier-Hein
Out of distribution detection for intra-operative functional imaging. CoRR (2019)
Lynton Ardizzone, Jakob Kruse, Sebastian J. Wirkert, Daniel Rahner, Eric W. Pellegrini, Ralf S. Klessen, Lena Maier-Hein, Carsten Rother, Ullrich Köthe
Analyzing Inverse Problems with Invertible Neural Networks. CoRR (2018)
Steffen Wolf, Constantin Pape, Alberto Bailoni, Nasim Rahaman, Anna Kreshuk, Ullrich Köthe, Fred A. Hamprecht
The Mutex Watershed: Efficient, Parameter-Free Image Partitioning. ECCV (4) (2018)
Nikola Krasowski, Thorsten Beier, Graham Knott, Ullrich Köthe, Fred A. Hamprecht, Anna Kreshuk
Neuron Segmentation With High-Level Biological Priors. IEEE Trans. Medical Imaging 37 (4) (2018)
Steffen Wolf, Lukas Schott, Ullrich Köthe, Fred A. Hamprecht
Learned Watershed: End-to-End Learning of Seeded Segmentation. ICCV (2017)
Steffen Wolf, Lukas Schott, Ullrich Köthe, Fred A. Hamprecht
Learned Watershed: End-to-End Learning of Seeded Segmentation. CoRR (2017)
Thorsten Beier, Björn Andres, Ullrich Köthe, Fred A. Hamprecht
An Efficient Fusion Move Algorithm for the Minimum Cost Lifted Multicut Problem. ECCV (2) (2016)
Michael Götz, Christian Weber, Franciszek Binczyk, Joanna Polanska, Rafal Tarnawski, Barbara Bobek-Billewicz, Ullrich Köthe, Jens Kleesiek, Bram Stieltjes, Klaus H. Maier-Hein
DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images. IEEE Trans. Medical Imaging 35 (1) (2016)
Nikola Krasowski, Thorsten Beier, Graham W. Knott, Ullrich Köthe, Fred A. Hamprecht, Anna Kreshuk
Improving 3D EM data segmentation by joint optimization over boundary evidence and biological priors. ISBI (2015)
Martin Schiegg, Ben Heuer, Carsten Haubold, Steffen Wolf, Ullrich Köthe, Fred A. Hamprecht
Proof-reading guidance in cell tracking by sampling from tracking-by-assignment models. ISBI (2015)
Martin Schiegg, Philipp Hanslovsky, Carsten Haubold, Ullrich Köthe, Lars Hufnagel, Fred A. Hamprecht
Graphical model for joint segmentation and tracking of multiple dividing cells. Bioinform. 31 (6) (2015)
Thorben Kröger, Jörg H. Kappes, Thorsten Beier, Ullrich Köthe, Fred A. Hamprecht
Asymmetric Cuts: Joint Image Labeling and Partitioning. GCPR (2014)
Christoph N. Straehle, Melih Kandemir, Ullrich Köthe, Fred A. Hamprecht
Multiple Instance Learning with Response-Optimized Random Forests. ICPR (2014)
Luca Fiaschi, Ferran Diego Andilla, Konstantin Gregor, Martin Schiegg, Ullrich Köthe, Marta Zlatic, Fred A. Hamprecht
Tracking Indistinguishable Translucent Objects over Time Using Weakly Supervised Structured Learning. CVPR (2014)
Thorsten Beier, Thorben Kröger, Jörg H. Kappes, Ullrich Köthe, Fred A. Hamprecht
Cut, Glue, & Cut: A Fast, Approximate Solver for Multicut Partitioning. CVPR (2014)
Thorben Kröger, Shawn Mikula, Winfried Denk, Ullrich Köthe, Fred A. Hamprecht
Learning to Segment Neurons with Non-local Quality Measures. MICCAI (2) (2013)
Christoph N. Straehle, Ullrich Köthe, Fred A. Hamprecht
Weakly Supervised Learning of Image Partitioning Using Decision Trees with Structured Split Criteria. ICCV (2013)
Christoph N. Straehle, Sven Peter, Ullrich Köthe, Fred A. Hamprecht
K-Smallest Spanning Tree Segmentations. GCPR (2013)
Xinghua Lou, Ullrich Köthe, Jochen Wittbrodt, Fred A. Hamprecht
Learning to segment dense cell nuclei with shape prior. CVPR (2012)
Björn Andres, Thorben Kröger, Kevin L. Briggman, Winfried Denk, Natalya Korogod, Graham Knott, Ullrich Köthe, Fred A. Hamprecht
Globally Optimal Closed-Surface Segmentation for Connectomics. ECCV (3) (2012)
Luca Fiaschi, Ullrich Köthe, Rahul Nair, Fred A. Hamprecht
Learning to count with regression forest and structured labels. ICPR (2012)
Björn Andres, Ullrich Köthe, Thorben Kröger, Moritz Helmstaedter, Kevin L. Briggman, Winfried Denk, Fred A. Hamprecht
3D segmentation of SBFSEM images of neuropil by a graphical model over supervoxel boundaries. Medical Image Anal. 16 (4) (2012)
Christoph N. Straehle, Ullrich Köthe, Graham Knott, Kevin L. Briggman, Winfried Denk, Fred A. Hamprecht
Seeded watershed cut uncertainty estimators for guided interactive segmentation. CVPR (2012)
Björn Andres, Jörg H. Kappes, Thorsten Beier, Ullrich Köthe, Fred A. Hamprecht
The Lazy Flipper: Efficient Depth-Limited Exhaustive Search in Discrete Graphical Models. ECCV (7) (2012)
Bernhard X. Kausler, Martin Schiegg, Björn Andres, Martin S. Lindner, Ullrich Köthe, Heike Leitte, Jochen Wittbrodt, Lars Hufnagel, Fred A. Hamprecht
A Discrete Chain Graph Model for 3d+t Cell Tracking with High Misdetection Robustness. ECCV (3) (2012)
Xinghua Lou, Luca Fiaschi, Ullrich Köthe, Fred A. Hamprecht
Quality Classification of Microscopic Imagery with Weakly Supervised Learning. MLMI (2012)
Björn Andres, Jörg H. Kappes, Thorsten Beier, Ullrich Köthe, Fred A. Hamprecht
Probabilistic image segmentation with closedness constraints. ICCV (2011)

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Publication Activity (10 Years)

Top Topics

Top Venues

Ullrich Köthe