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ORCIDUllrich Köthe
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- affiliation: Heidelberg University, HCI/IWR, Germany
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2020 – today
- 2024
[c81]Felix Draxler, Peter Sorrenson, Lea Zimmermann, Armand Rousselot, Ullrich Köthe:
Free-form Flows: Make Any Architecture a Normalizing Flow. AISTATS 2024: 2197-2205- [c80]Peter Sorrenson, Felix Draxler, Armand Rousselot, Sander Hummerich, Lea Zimmermann, Ullrich Köthe:
Lifting Architectural Constraints of Injective Flows. ICLR 2024 - [c79]Felix Draxler, Stefan Wahl, Christoph Schnörr, Ullrich Köthe:
On the Universality of Volume-Preserving and Coupling-Based Normalizing Flows. ICML 2024 - [c78]Marvin Schmitt, Desi R. Ivanova, Daniel Habermann, Ullrich Köthe, Paul-Christian Bürkner, Stefan T. Radev:
Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference. ICML 2024 - [e2]Ullrich Köthe
, Carsten Rother:
Pattern Recognition - 45th DAGM German Conference, DAGM GCPR 2023, Heidelberg, Germany, September 19-22, 2023, Proceedings. Lecture Notes in Computer Science 14264, Springer 2024, ISBN 978-3-031-54604-4 [contents] - [i51]Felix Draxler, Stefan Wahl, Christoph Schnörr, Ullrich Köthe:
On the Universality of Coupling-based Normalizing Flows. CoRR abs/2402.06578 (2024) - [i50]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 abs/2403.07434 (2024) - [i49]Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev:
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation. CoRR abs/2406.03154 (2024) - [i48]Peter Lorenz, Mario Fernandez, Jens Müller, Ullrich Köthe:
Deciphering the Definition of Adversarial Robustness for post-hoc OOD Detectors. CoRR abs/2406.15104 (2024) - [i47]Peter Sorrenson, Daniel Behrend-Uriarte, Christoph Schnörr, Ullrich Köthe:
Learning Distances from Data with Normalizing Flows and Score Matching. CoRR abs/2407.09297 (2024) - [i46]Daniel Galperin, Ullrich Köthe:
Analyzing Generative Models by Manifold Entropic Metrics. CoRR abs/2410.19426 (2024) - [i45]Stefan Wahl, Armand Rousselot, Felix Draxler, Ullrich Köthe:
TRADE: Transfer of Distributions between External Conditions with Normalizing Flows. CoRR abs/2410.19492 (2024) - 2023
- [j18]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): 5702 (2023) - [j17]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) - [j16]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) - [j15]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): 4903-4917 (2023) - [c77]Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev:
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks. DAGM 2023: 541-557 - [c76]Felix Draxler, Lars Kühmichel, Armand Rousselot, Jens Müller, Christoph Schnörr, Ullrich Köthe:
On the Convergence Rate of Gaussianization with Random Rotations. ICML 2023: 8449-8468 - [c75]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: 770-780 - [c74]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: 1695-1706 - [i44]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 abs/2301.13462 (2023) - [i43]Stefan T. Radev, Marvin Schmitt, Valentin Pratz, Umberto Picchini, Ullrich Köthe, Paul-Christian Bürkner:
JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models. CoRR abs/2302.09125 (2023) - [i42]Jens Müller, Stefan T. Radev, Robert Schmier, Felix Draxler, Carsten Rother, Ullrich Köthe:
Finding Competence Regions in Domain Generalization. CoRR abs/2303.09989 (2023) - [i41]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 abs/2303.10191 (2023) - [i40]The-Gia Leo Nguyen, Lynton Ardizzone, Ullrich Köthe:
Training Invertible Neural Networks as Autoencoders. CoRR abs/2303.11239 (2023) - [i39]Peter Sorrenson, Felix Draxler, Armand Rousselot, Sander Hummerich, Lea Zimmermann, Ullrich Köthe:
Maximum Likelihood Training of Autoencoders. CoRR abs/2306.01843 (2023) - [i38]Felix Draxler, Lars Kühmichel, Armand Rousselot, Jens Müller, Christoph Schnörr, Ullrich Köthe:
On the Convergence Rate of Gaussianization with Random Rotations. CoRR abs/2306.13520 (2023) - [i37]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 abs/2306.16015 (2023) - [i36]Ullrich Köthe:
A Review of Change of Variable Formulas for Generative Modeling. CoRR abs/2308.02652 (2023) - [i35]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 abs/2309.09764 (2023) - [i34]Marvin Schmitt, Daniel Habermann, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev:
Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference. CoRR abs/2310.04395 (2023) - [i33]Lasse Elsemüller, Hans Olischläger, Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev:
Sensitivity-Aware Amortized Bayesian Inference. CoRR abs/2310.11122 (2023) - [i32]Felix Draxler, Peter Sorrenson, Lea Zimmermann, Armand Rousselot, Ullrich Köthe:
Free-form Flows: Make Any Architecture a Normalizing Flow. CoRR abs/2310.16624 (2023) - [i31]Marvin Schmitt, Valentin Pratz, Ullrich Köthe, Paul-Christian Bürkner, Stefan T. Radev:
Consistency Models for Scalable and Fast Simulation-Based Inference. CoRR abs/2312.05440 (2023) - [i30]Peter Sorrenson, Felix Draxler, Armand Rousselot, Sander Hummerich, Ullrich Köthe:
Learning Distributions on Manifolds with Free-form Flows. CoRR abs/2312.09852 (2023) - [i29]Jens Müller, Lars Kühmichel, Martin Rohbeck, Stefan T. Radev, Ullrich Köthe:
Towards Context-Aware Domain Generalization: Representing Environments with Permutation-Invariant Networks. CoRR abs/2312.10107 (2023) - 2022
- [j14]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): 1452-1466 (2022) - [c73]Titus Leistner, Radek Mackowiak, Lynton Ardizzone, Ullrich Köthe, Carsten Rother:
Towards Multimodal Depth Estimation from Light Fields. CVPR 2022: 12943-12951 - [c72]Malte Tölle, Ullrich Köthe, Florian André, Benjamin Meder, Sandy Engelhardt:
Content-Aware Differential Privacy with Conditional Invertible Neural Networks. DeCaF/FAIR@MICCAI 2022: 89-99 - [c71]Felix Draxler, Christoph Schnörr, Ullrich Köthe:
Whitening Convergence Rate of Coupling-based Normalizing Flows. NeurIPS 2022 - [i28]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 abs/2202.00027 (2022) - [i27]Titus Leistner, Radek Mackowiak, Lynton Ardizzone, Ullrich Köthe, Carsten Rother:
Towards Multimodal Depth Estimation from Light Fields. CoRR abs/2203.16542 (2022) - [i26]Malte Tölle, Ullrich Köthe, Florian André, Benjamin Meder, Sandy Engelhardt:
Content-Aware Differential Privacy with Conditional Invertible Neural Networks. CoRR abs/2207.14625 (2022) - [i25]Robert Schmier, Ullrich Köthe, Christoph-Nikolas Straehle:
Anomaly Detection using Contrastive Normalizing Flows. CoRR abs/2208.14024 (2022) - [i24]Felix Draxler, Christoph Schnörr, Ullrich Köthe:
Whitening Convergence Rate of Coupling-based Normalizing Flows. CoRR abs/2210.14032 (2022) - 2021
- [j13]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): 3724-3738 (2021) - [j12]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) - [c70]Jakob Kruse, Gianluca Detommaso, Ullrich Köthe, Robert Scheichl:
HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference. AAAI 2021: 8191-8199 - [c69]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: 330-335 - [c68]Radek Mackowiak, Lynton Ardizzone, Ullrich Köthe, Carsten Rother:
Generative Classifiers as a Basis for Trustworthy Image Classification. CVPR 2021: 2971-2981 - [c67]Jens Müller, Robert Schmier, Lynton Ardizzone, Carsten Rother, Ullrich Köthe:
Learning Robust Models Using the Principle of Independent Causal Mechanisms. GCPR 2021: 79-110 - [i23]Jakob Kruse, Lynton Ardizzone, Carsten Rother, Ullrich Köthe:
Benchmarking Invertible Architectures on Inverse Problems. CoRR abs/2101.10763 (2021) - [i22]Lynton Ardizzone, Jakob Kruse, Carsten T. Lüth, Niels Bracher, Carsten Rother, Ullrich Köthe:
Conditional Invertible Neural Networks for Diverse Image-to-Image Translation. CoRR abs/2105.02104 (2021) - [i21]Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev:
BayesFlow can reliably detect Model Misspecification and Posterior Errors in Amortized Bayesian Inference. CoRR abs/2112.08866 (2021) - 2020
- [c66]Felix Draxler, Jonathan Schwarz, Christoph Schnörr, Ullrich Köthe:
Characterizing the Role of a Single Coupling Layer in Affine Normalizing Flows. GCPR 2020: 1-14 - [c65]Jonathan Schwarz, Felix Draxler, Ullrich Köthe, Christoph Schnörr:
Riemannian SOS-Polynomial Normalizing Flows. GCPR 2020: 218-231 - [c64]Lynton Ardizzone, Jakob Kruse, Carsten T. Lüth, Niels Bracher, Carsten Rother, Ullrich Köthe:
Conditional Invertible Neural Networks for Diverse Image-to-Image Translation. GCPR 2020: 373-387 - [c63]Peter Sorrenson, Carsten Rother, Ullrich Köthe:
Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN). ICLR 2020 - [c62]Lynton Ardizzone, Radek Mackowiak, Carsten Rother, Ullrich Köthe:
Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification. NeurIPS 2020 - [i20]Peter Sorrenson, Carsten Rother, Ullrich Köthe:
Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN). CoRR abs/2001.04872 (2020) - [i19]Lynton Ardizzone, Radek Mackowiak, Ullrich Köthe, Carsten Rother:
Exact Information Bottleneck with Invertible Neural Networks: Getting the Best of Discriminative and Generative Modeling. CoRR abs/2001.06448 (2020) - [i18]Stefan T. Radev, Ulf K. Mertens, Andreas Voss, Lynton Ardizzone, Ullrich Köthe:
BayesFlow: Learning complex stochastic models with invertible neural networks. CoRR abs/2003.06281 (2020) - [i17]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 abs/2004.10629 (2020) - [i16]Radek Mackowiak, Lynton Ardizzone, Ullrich Köthe, Carsten Rother:
Generative Classifiers as a Basis for Trustworthy Computer Vision. CoRR abs/2007.15036 (2020) - [i15]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 abs/2010.00300 (2020) - [i14]Jens Müller, Robert Schmier, Lynton Ardizzone, Carsten Rother, Ullrich Köthe:
Learning Robust Models Using The Principle of Independent Causal Mechanisms. CoRR abs/2010.07167 (2020) - [i13]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 abs/2011.05110 (2020) - [i12]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 abs/2012.08195 (2020)
2010 – 2019
- 2019
- [j11]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): 997-1007 (2019) - [c61]Ricard Durall, Franz-Josef Pfreundt, Ullrich Köthe, Janis Keuper:
Object Segmentation Using Pixel-Wise Adversarial Loss. GCPR 2019: 303-316 - [c60]The-Gia Leo Nguyen, Lynton Ardizzone, Ullrich Köthe:
Training Invertible Neural Networks as Autoencoders. GCPR 2019: 442-455 - [c59]Lynton Ardizzone, Jakob Kruse, Carsten Rother, Ullrich Köthe:
Analyzing Inverse Problems with Invertible Neural Networks. ICLR (Poster) 2019 - [c58]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: 75-82 - [i11]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 abs/1903.03441 (2019) - [i10]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 abs/1904.12654 (2019) - [i9]Gianluca Detommaso, Jakob Kruse, Lynton Ardizzone, Carsten Rother, Ullrich Köthe, Robert Scheichl:
HINT: Hierarchical Invertible Neural Transport for General and Sequential Bayesian inference. CoRR abs/1905.10687 (2019) - [i8]Lynton Ardizzone, Carsten T. Lüth, Jakob Kruse, Carsten Rother, Ullrich Köthe:
Guided Image Generation with Conditional Invertible Neural Networks. CoRR abs/1907.02392 (2019) - [i7]Ricard Durall, Franz-Josef Pfreundt, Ullrich Köthe, Janis Keuper:
Object Segmentation using Pixel-wise Adversarial Loss. CoRR abs/1909.10341 (2019) - [i6]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 abs/1911.01877 (2019) - 2018
- [j10]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): 829-839 (2018) - [c57]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: 571-587 - [i5]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 abs/1808.04730 (2018) - 2017
- [c56]Steffen Wolf, Lukas Schott, Ullrich Köthe, Fred A. Hamprecht:
Learned Watershed: End-to-End Learning of Seeded Segmentation. ICCV 2017: 2030-2038 - [i4]Steffen Wolf, Lukas Schott, Ullrich Köthe, Fred A. Hamprecht:
Learned Watershed: End-to-End Learning of Seeded Segmentation. CoRR abs/1704.02249 (2017) - 2016
- [j9]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): 184-196 (2016) - [c55]Thorsten Beier, Björn Andres, Ullrich Köthe, Fred A. Hamprecht:
An Efficient Fusion Move Algorithm for the Minimum Cost Lifted Multicut Problem. ECCV (2) 2016: 715-730 - 2015
- [j8]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): 948-956 (2015) - [c54]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: 394-398 - [c53]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: 536-539 - 2014
- [c52]Thorsten Beier, Thorben Kröger, Jörg H. Kappes, Ullrich Köthe, Fred A. Hamprecht:
Cut, Glue, & Cut: A Fast, Approximate Solver for Multicut Partitioning. CVPR 2014: 73-80 - [c51]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: 2736-2743 - [c50]Thorben Kröger, Jörg H. Kappes, Thorsten Beier, Ullrich Köthe, Fred A. Hamprecht:
Asymmetric Cuts: Joint Image Labeling and Partitioning. GCPR 2014: 199-211 - [c49]Christoph N. Straehle, Melih Kandemir, Ullrich Köthe, Fred A. Hamprecht:
Multiple Instance Learning with Response-Optimized Random Forests. ICPR 2014: 3768-3773 - 2013
- [c48]Christoph N. Straehle, Sven Peter, Ullrich Köthe, Fred A. Hamprecht:
K-Smallest Spanning Tree Segmentations. GCPR 2013: 375-384 - [c47]Christoph N. Straehle, Ullrich Köthe, Fred A. Hamprecht:
Weakly Supervised Learning of Image Partitioning Using Decision Trees with Structured Split Criteria. ICCV 2013: 1849-1856 - [c46]Thorben Kröger, Shawn Mikula, Winfried Denk, Ullrich Köthe, Fred A. Hamprecht:
Learning to Segment Neurons with Non-local Quality Measures. MICCAI (2) 2013: 419-427 - 2012
- [j7]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): 796-805 (2012) - [c45]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: 765-772 - [c44]Xinghua Lou, Ullrich Köthe, Jochen Wittbrodt, Fred A. Hamprecht:
Learning to segment dense cell nuclei with shape prior. CVPR 2012: 1012-1018 - [c43]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: 144-157 - [c42]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: 154-166 - [c41]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: 778-791 - [c40]Luca Fiaschi, Ullrich Köthe, Rahul Nair, Fred A. Hamprecht:
Learning to count with regression forest and structured labels. ICPR 2012: 2685-2688 - [c39]Xinghua Lou, Luca Fiaschi, Ullrich Köthe, Fred A. Hamprecht:
Quality Classification of Microscopic Imagery with Weakly Supervised Learning. MLMI 2012: 176-183 - [e1]Ullrich Köthe, Annick Montanvert, Pierre Soille:
Applications of Discrete Geometry and Mathematical Morphology - First International Workshop, WADGMM 2010, Istanbul, Turkey, August 22, 2010, Revised Selected Papers. Lecture Notes in Computer Science 7346, Springer 2012, ISBN 978-3-642-32312-6 [contents] - 2011
- [j6]Björn Voss, Michael Hanselmann, Bernhard Y. Renard, Martin S. Lindner, Ullrich Köthe, Marc Kirchner, Fred A. Hamprecht:
SIMA: Simultaneous Multiple Alignment of LC/MS Peak Lists. Bioinform. 27(7): 987-993 (2011) - [c38]Björn Andres, Jörg H. Kappes, Thorsten Beier, Ullrich Köthe, Fred A. Hamprecht:
Probabilistic image segmentation with closedness constraints. ICCV 2011: 2611-2618 - [c37]Anna Kreshuk, Christoph N. Straehle, Christoph Sommer, Ullrich Köthe, Graham Knott, Fred A. Hamprecht:
Automated segmentation of synapses in 3D EM data. ISBI 2011: 220-223 - [c36]Christoph Sommer, Christoph N. Straehle, Ullrich Köthe, Fred A. Hamprecht:
Ilastik: Interactive learning and segmentation toolkit. ISBI 2011: 230-233 - [c35]Xinghua Lou, Frederik O. Kaster, Martin S. Lindner, Bernhard X. Kausler, Ullrich Köthe, Burkhard Hockendorf, Jochen Wittbrodt, Heike Jänicke, Fred A. Hamprecht:
Deltr: Digital embryo lineage tree reconstructor. ISBI 2011: 1557-1560 - [c34]Christoph N. Straehle, Ullrich Köthe, Graham Knott, Fred A. Hamprecht:
Carving: Scalable Interactive Segmentation of Neural Volume Electron Microscopy Images. MICCAI (1) 2011: 653-660 - [c33]