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Christian Etmann
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2020 – today
- 2023
- [c3]Christina Runkel, Michael Möller, Carola-Bibiane Schönlieb, Christian Etmann:
Learning Posterior Distributions in Underdetermined Inverse Problems. SSVM 2023: 187-209 - 2022
- [i13]Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb, Christian Etmann:
Non-Uniform Diffusion Models. CoRR abs/2207.09786 (2022) - 2021
- [j2]Michael Roberts, Derek Driggs, Matthew Thorpe, Julian D. Gilbey, Michael Yeung, Stephan Ursprung, Angelica I. Avilés-Rivero, Christian Etmann, Cathal McCague, Lucian Beer, Jonathan R. Weir-McCall, Zhongzhao Teng, Effrossyni Gkrania-Klotsas, James H. F. Rudd, Evis Sala, Carola-Bibiane Schönlieb:
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat. Mach. Intell. 3(3): 199-217 (2021) - [i12]Christina Runkel, Christian Etmann, Michael Möller, Carola-Bibiane Schönlieb:
Depthwise Separable Convolutions Allow for Fast and Memory-Efficient Spectral Normalization. CoRR abs/2102.06496 (2021) - [i11]Elena Celledoni, Matthias J. Ehrhardt, Christian Etmann, Brynjulf Owren, Carola-Bibiane Schönlieb, Ferdia Sherry:
Equivariant neural networks for inverse problems. CoRR abs/2102.11504 (2021) - [i10]Jan Stanczuk, Christian Etmann, Lisa Maria Kreusser, Carola-Bibiane Schönlieb:
Wasserstein GANs Work Because They Fail (to Approximate the Wasserstein Distance). CoRR abs/2103.01678 (2021) - [i9]Georgios Batzolis, Marcello Carioni, Christian Etmann, Soroosh Afyouni, Zoe Kourtzi, Carola-Bibiane Schönlieb:
CAFLOW: Conditional Autoregressive Flows. CoRR abs/2106.02531 (2021) - [i8]Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb, Christian Etmann:
Conditional Image Generation with Score-Based Diffusion Models. CoRR abs/2111.13606 (2021) - 2020
- [c2]Christian Etmann, Rihuan Ke, Carola-Bibiane Schönlieb:
iUNets: Learnable Invertible Up- and Downsampling for Large-Scale Inverse Problems. MLSP 2020: 1-6 - [i7]Christian Etmann, Rihuan Ke, Carola-Bibiane Schönlieb:
iUNets: Fully invertible U-Nets with Learnable Up- and Downsampling. CoRR abs/2005.05220 (2020) - [i6]Elena Celledoni, Matthias J. Ehrhardt, Christian Etmann, Robert I. McLachlan, Brynjulf Owren, Carola-Bibiane Schönlieb, Ferdia Sherry:
Structure preserving deep learning. CoRR abs/2006.03364 (2020) - [i5]Michael Roberts, Derek Driggs, Matthew Thorpe, Julian D. Gilbey, Michael Yeung, Stephan Ursprung, Angelica I. Avilés-Rivero, Christian Etmann, Cathal McCague, Lucian Beer, Jonathan R. Weir-McCall, Zhongzhao Teng, James H. F. Rudd, Evis Sala, Carola-Bibiane Schönlieb:
Machine learning for COVID-19 detection and prognostication using chest radiographs and CT scans: a systematic methodological review. CoRR abs/2008.06388 (2020)
2010 – 2019
- 2019
- [c1]Christian Etmann, Sebastian Lunz, Peter Maass, Carola Schönlieb:
On the Connection Between Adversarial Robustness and Saliency Map Interpretability. ICML 2019: 1823-1832 - [i4]Christian Etmann, Sebastian Lunz, Peter Maass, Carola-Bibiane Schönlieb:
On the Connection Between Adversarial Robustness and Saliency Map Interpretability. CoRR abs/1905.04172 (2019) - [i3]Christian Etmann:
A Closer Look at Double Backpropagation. CoRR abs/1906.06637 (2019) - [i2]Christian Etmann, Maximilian Schmidt, Jens Behrmann, Tobias Boskamp, Lena Hauberg-Lotte, Annette Peter, Rita Casadonte, Jörg Kriegsmann, Peter Maass:
Deep Relevance Regularization: Interpretable and Robust Tumor Typing of Imaging Mass Spectrometry Data. CoRR abs/1912.05459 (2019) - 2018
- [j1]Jens Behrmann, Christian Etmann, Tobias Boskamp, Rita Casadonte, Jörg Kriegsmann, Peter Maaß:
Deep learning for tumor classification in imaging mass spectrometry. Bioinform. 34(7): 1215-1223 (2018) - 2017
- [i1]Jens Behrmann, Christian Etmann, Tobias Boskamp, Rita Casadonte, Jörg Kriegsmann, Peter Maass:
Deep Learning for Tumor Classification in Imaging Mass Spectrometry. CoRR abs/1705.01015 (2017)
Coauthor Index
aka: Carola Schönlieb
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