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2021
A. Vijayan, Tofanelli, R., Strauss, S., Cerrone, L., Wolny, A., Strohmeier, J., Kreshuk, A., Hamprecht, F. A., Smith, R. S., and Schneitz, K., “A Digital 3D Reference Atlas Reveals Cellular Growth Patterns Shaping the Arabidopsis Ovule”, eLife, 2021.
M. Kandemir, Agkül, A., Haußmann, M., and Ünal, G., “Evidential Turing Processes”. arXiv preprint, 2021.
L. M. Schütz, Louveaux, M., Vilches-Barro, A., Bouziri, S., Cerrone, L., Wolny, A., Kreshuk, A., Hamprecht, F. A., and Maizel, A., “Integration of Cell Growth and Asymmetric Division during Lateral Root Initiation in Arabidopsis thaliana”, Plant and Cell Physiology, vol. 62, no. 8, pp. 1269-1279, 2021.
M. Haußmann, Gerwinn, S., Look, A., Rakitsch, B., and Kandemir, M., “Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes”, International Conference on Artificial Intelligence and Statistics , vol. PMLR 130. pp. 478-486, 2021.
C. Pape, Remme, R., Wolny, A., Olberg, S., Wolf, S., Cerrone, L., Cortese, M., Klaus, S., Lucic, B., Ullrich, S., Anders-Össwein, M., Wolf, S., Cerikan, B., Neufeldt, C. J., Ganter, M., Schnitzler, P., Merle, U., Lusic, M., Boulant, S., Stanifer, M., Bartenschlager, R., Hamprecht, F. A., Kreshuk, A., Tischer, C., Kräusslich, H. - G., Müller, B., and Laketa, V., “Microscopy-based assay for semi-quantitative detection of SARS-CoV-2 specific antibodies in human sera”, BioEssays, vol. 43, no. 3, 2021.
C. Pape, Remme, R., Wolny, A., Olberg, S., Wolf, S., Cerrone, L., Cortese, M., Klaus, S., Lucic, B., Ullrich, S., Anders-Össwein, M., Wolf, S., Cerikan, B., Neufeldt, C. J., Ganter, M., Schnitzler, P., Merle, U., Lusic, M., Boulant, S., Stanifer, M., Bartenschlager, R., Hamprecht, F. A., Kreshuk, A., Tischer, C., Kräusslich, H. - G., Müller, B., and Laketa, V., “Microscopy-based assay for semi-quantitative detection of SARS-CoV-2 specific antibodies in human sera”, BioEssays, vol. 43, no. 3, 2021.
C. Pape, Remme, R., Wolny, A., Olberg, S., Wolf, S., Cerrone, L., Cortese, M., Klaus, S., Lucic, B., Ullrich, S., Anders-Össwein, M., Wolf, S., Cerikan, B., Neufeldt, C. J., Ganter, M., Schnitzler, P., Merle, U., Lusic, M., Boulant, S., Stanifer, M., Bartenschlager, R., Hamprecht, F. A., Kreshuk, A., Tischer, C., Kräusslich, H. - G., Müller, B., and Laketa, V., “Microscopy-based assay for semi-quantitative detection of SARS-CoV-2 specific antibodies in human sera”, BioEssays, vol. 43, no. 3, 2021.
D. Kotovenko, Wright, M., Heimbrecht, A., and Ommer, B., “Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2021.
M. Amirul Islam, Kowal, M., Esser, P., Jia, S., Ommer, B., Derpanis, K. G., and Bruce, N., “Shape or Texture: Understanding Discriminative Features in CNNs”, International Conference on Learning Representations (ICLR). 2021.
2020
A. Wolny, Cerrone, L., Vijayan, A., Tofanelli, R., Vilches-Barro, A., Louveaux, M., Wenzel, C., Strauss, S., Wilson-Sanchez, D., Lymbouridou, R., Steigleder, S. S., Pape, C., Bailoni, A., Duran-Nebreda, S., Bassel, G. W., Lohmann, J. U., Tsiantis, M., Hamprecht, F. A., Schneitz, K., Maizel, A., and Kreshuk, A., “Accurate and Versatile 3D Segmentation of Plant Tissues at Cellular Resolution”, eLife, vol. 9, 2020.
A. Krull, Hirsch, P., Rother, C., Schiffrin, A., and Krull, C., “Artificial-intelligence-driven scanning probe microscopy”, Communications Physics, vol. 3, 2020.
A. Krull, Hirsch, P., Rother, C., Schiffrin, A., and Krull, C., “Artificial-intelligence-driven scanning probe microscopy”, Communications Physics, vol. 3, 2020.
S. T. Radev, Mertens, U. K., Voss, A., Ardizzone, L., and Köthe, U., “BayesFlow: Learning complex stochastic models with invertible neural networks”, 2020.PDF icon PDF (5.36 MB)
M. Haußmann, Gerwinn, S., and Kandemir, M., “Bayesian Evidential Deep Learning with PAC Regularization ”, 3rd Symposium on Advances in Approximate Bayesian Inference . 2020.
C. Kamann and Rother, C., “Benchmarking the Robustness of Semantic Segmentation Models”, in CVPR 2020, 2020.PDF icon PDF (3.61 MB)
F. Kluger, Brachmann, E., Ackermann, H., Rother, C., Yang, M. Ying, and Rosenhahn, B., “CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus”, in CVPR 2020, 2020.PDF icon PDF (9.95 MB)
T. Dencker, Klinkisch, P., Maul, S. M., and Ommer, B., “Deep learning of cuneiform sign detection with weak supervision using transliteration alignment”, PLoS ONE, vol. 15, no. 12, 2020.
S. Bollweg, Haußmann, M., Kasieczka, G., Luchmann, M., Plehn, T., and Thompson, J., “Deep-Learning Jets with Uncertainties and More”, SciPost Phys, vol. 8, no. 1, 2020.PDF icon Technical Report (1.65 MB)
E. Kirschbaum, Bailoni, A., and Hamprecht, F. A., “DISCo: Deep Learning, Instance Segmentation, and Correlations for Cell Segmentation in Calcium Imaging”, MICCAI. Proceedings. pp. 151-162, 2020.
P. Sorrenson, Rother, C., and Köthe, U., “Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)”, in Intl. Conf. Learning Representations (ICLR), 2020.PDF icon PDF (2.43 MB)
T. M. Hehn, Kooij, J. F. P., and Hamprecht, F. A., “End-to-End Learning of Decision Trees and Forests”, International Journal of Computer Vision, vol. 128, pp. 997-1011, 2020.
L. Ardizzone, Mackowiak, R., Rother, C., and Köthe, U., “Exact Information Bottleneck with Invertible Neural Networks: Getting the Best of Discriminative and Generative Modeling”, 2020.PDF icon PDF (2.87 MB)
H. Schilling, Gutsche, M., Brock, A., Späth, D., Rother, C., and Krispin, K., “Mind the Gap – A Benchmark for Dense Depth Prediction beyond Lidar”, in 2nd Workshop on Safe Artificial Intelligence for Automated Driving, in conjunction with CVPR 2020, 2020.
S. Wolf, Bailoni, A., Pape, C., Rahaman, N., Kreshuk, A., Köthe, U., and Hamprecht, F. A., “The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, pp. 3724-3738, 2020.PDF icon Technical Report (2.58 MB)
S. Wolf, Bailoni, A., Pape, C., Rahaman, N., Kreshuk, A., Köthe, U., and Hamprecht, F. A., “The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, pp. 3724-3738, 2020.PDF icon Technical Report (2.58 MB)
A. Bailoni, Pape, C., Wolf, S., Kreshuk, A., and Hamprecht, F. A., “Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks”, GCPR, vol. 12544. Springer, pp. 331-344, 2020.
S. K. Mustikovela, Jampani, V., De Mello, S., Liu, S., Iqbal, U., Rother, C., and Kautz, J., “Self-Supervised Viewpoint Learning From Image Collections”, in CONSAC, 2020.PDF icon PDF (8.77 MB)
S. Wolf, Li, Y., Pape, C., Bailoni, A., Kreshuk, A., and Hamprecht, F. A., “The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation”, ECCV. Proceedings. pp. 208-224, 2020.
2019
K. E. Krall, Smith, A. W., Takagaki, N., and Jähne, B., “Air–sea gas exchange at wind speeds up to 85 m/s”, Ocean Science, vol. 15, p. 1783-–1799, 2019.
M. Haußmann, Gerwinn, S., and Kandemir, M., “Bayesian Prior Networks with PAC Training”, arXiv preprint arXiv:1906.00816, 2019.
J. Kruse, Ardizzone, L., Rother, C., and Köthe, U., “Benchmarking Invertible Architectures on Inverse Problems”, i, 2019.
J. Kruse, Ardizzone, L., Rother, C., and Köthe, U., “Benchmarking Invertible Architectures on Inverse Problems”, i, 2019.
C. Kamann and Rother, C., “Benchmarking the Robustness of Semantic Segmentation Models”, 2019.
A. L. Bendinger, Debus, C., Glowa, C., Karger, C. P., Peter, J., and Storath, M., “Bolus arrival time estimation in dynamic contrast-enhanced magnetic resonance imaging of small animals based on spline models, in press”, Physics in Medicine and Biology, vol. 64, no. 4, 2019.
J. Kleesiek, Morshuis, J. Nikolas, Isensee, F., Deike-Hofmann, K., Paech, D., Kickingereder, P., Köthe, U., Rother, C., Forsting, M., Wick, W., Bendszus, M., Schlemmer, H. Peter, and Radbruch, A., “Can Virtual Contrast Enhancement in Brain MRI Replace Gadolinium?: A Feasibility Study”, Investigative Radiology, vol. 54, pp. 653–660, 2019.

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