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Journal Articles
- 2021
[j2]Sebastian Mathias Keller
, Maxim Samarin, Fabricio Arend Torres, Mario Wieser, Volker Roth:
Learning Extremal Representations with Deep Archetypal Analysis. Int. J. Comput. Vis. 129(4): 805-820 (2021)- 2020
- [j1]Sonali Parbhoo, Mario Wieser, Aleksander Wieczorek, Volker Roth:
Information Bottleneck for Estimating Treatment Effects with Systematically Missing Covariates. Entropy 22(4): 389 (2020)
Conference and Workshop Papers
- 2023
- [c9]Daniel Siegismund, Mario Wieser, Stephan Heyse, Stephan Steigele:
Learning Channel Importance for High Content Imaging with Interpretable Deep Input Channel Mixing. DAGM 2023: 335-347 - 2022
- [c8]Daniel Siegismund, Mario Wieser, Stephan Heyse, Stephan Steigele:
Self-Supervised Representation Learning for High-Content Screening. MIDL 2022: 1108-1124 - [c7]Mario Wieser, Daniel Siegismund, Stephan Heyse, Stephan Steigele:
Vision Transformers Show Improved Robustness in High-Content Image Analysis. SDS 2022: 71-72 - 2021
- [c6]Maxim Samarin, Vitali Nesterov, Mario Wieser, Aleksander Wieczorek, Sonali Parbhoo, Volker Roth:
Learning Conditional Invariance Through Cycle Consistency. GCPR 2021: 376-391 - 2020
- [c5]Sonali Parbhoo, Mario Wieser, Volker Roth, Finale Doshi-Velez:
Transfer Learning from Well-Curated to Less-Resourced Populations with HIV. MLHC 2020: 589-609 - [c4]Mario Wieser, Sonali Parbhoo, Aleksander Wieczorek, Volker Roth:
Inverse Learning of Symmetries. NeurIPS 2020 - 2019
- [c3]Adam Kortylewski, Aleksander Wieczorek, Mario Wieser, Clemens Blumer, Sonali Parbhoo, Andreas Morel-Forster, Volker Roth, Thomas Vetter:
Greedy Structure Learning of Hierarchical Compositional Models. CVPR 2019: 11612-11621 - [c2]Sebastian Mathias Keller, Maxim Samarin, Mario Wieser, Volker Roth:
Deep Archetypal Analysis. GCPR 2019: 171-185 - 2018
- [c1]Aleksander Wieczorek, Mario Wieser, Damian Murezzan, Volker Roth:
Learning Sparse Latent Representations with the Deep Copula Information Bottleneck. ICLR (Poster) 2018
Informal and Other Publications
- 2023
- [i10]Daniel Siegismund, Mario Wieser, Stephan Heyse, Stephan Steigele:
Learning Channel Importance for High Content Imaging with Interpretable Deep Input Channel Mixing. CoRR abs/2308.16637 (2023) - 2021
- [i9]Maxim Samarin, Vitali Nesterov, Mario Wieser, Aleksander Wieczorek, Sonali Parbhoo, Volker Roth:
Learning Conditional Invariance through Cycle Consistency. CoRR abs/2111.13185 (2021) - 2020
- [i8]Sebastian Mathias Keller, Maxim Samarin, Fabricio Arend Torres, Mario Wieser, Volker Roth:
Learning Extremal Representations with Deep Archetypal Analysis. CoRR abs/2002.00815 (2020) - [i7]Mario Wieser, Sonali Parbhoo, Aleksander Wieczorek, Volker Roth:
Inverse Learning of Symmetry Transformations. CoRR abs/2002.02782 (2020) - [i6]Vitali Nesterov, Mario Wieser, Volker Roth:
3DMolNet: A Generative Network for Molecular Structures. CoRR abs/2010.06477 (2020) - 2019
- [i5]Sebastian Mathias Keller, Maxim Samarin, Mario Wieser, Volker Roth:
Deep Archetypal Analysis. CoRR abs/1901.10799 (2019) - 2018
- [i4]Aleksander Wieczorek, Mario Wieser, Damian Murezzan, Volker Roth:
Learning Sparse Latent Representations with the Deep Copula Information Bottleneck. CoRR abs/1804.06216 (2018) - [i3]Sonali Parbhoo, Mario Wieser, Volker Roth:
Causal Deep Information Bottleneck. CoRR abs/1807.02326 (2018) - [i2]Adam Kortylewski, Mario Wieser, Andreas Morel-Forster, Aleksander Wieczorek, Sonali Parbhoo, Volker Roth, Thomas Vetter:
Informed MCMC with Bayesian Neural Networks for Facial Image Analysis. CoRR abs/1811.07969 (2018) - [i1]Sonali Parbhoo, Mario Wieser, Volker Roth:
Estimating Causal Effects With Partial Covariates For Clinical Interpretability. CoRR abs/1811.10347 (2018)
Coauthor Index
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