Christian Etmann

Publication Activity (10 Years)

Years Active: 2017-2023
Publications (10 Years): 18

Top Topics

Mass Spectrometry

Posterior Distribution

Diffusion Models

Top Venues

Publications

Christina Runkel, Michael Möller, Carola-Bibiane Schönlieb, Christian Etmann
Learning Posterior Distributions in Underdetermined Inverse Problems. SSVM (2023)
Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb, Christian Etmann
Non-Uniform Diffusion Models. CoRR (2022)
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) (2021)
Elena Celledoni, Matthias J. Ehrhardt, Christian Etmann, Brynjulf Owren, Carola-Bibiane Schönlieb, Ferdia Sherry
Equivariant neural networks for inverse problems. CoRR (2021)
Georgios Batzolis, Marcello Carioni, Christian Etmann, Soroosh Afyouni, Zoe Kourtzi, Carola-Bibiane Schönlieb
CAFLOW: Conditional Autoregressive Flows. CoRR (2021)
Jan Stanczuk, Christian Etmann, Lisa Maria Kreusser, Carola-Bibiane Schönlieb
Wasserstein GANs Work Because They Fail (to Approximate the Wasserstein Distance). CoRR (2021)
Christina Runkel, Christian Etmann, Michael Möller, Carola-Bibiane Schönlieb
Depthwise Separable Convolutions Allow for Fast and Memory-Efficient Spectral Normalization. CoRR (2021)
Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb, Christian Etmann
Conditional Image Generation with Score-Based Diffusion Models. CoRR (2021)
Christian Etmann, Rihuan Ke, Carola-Bibiane Schönlieb
iUNets: Learnable Invertible Up- and Downsampling for Large-Scale Inverse Problems. MLSP (2020)
Christian Etmann, Rihuan Ke, Carola-Bibiane Schönlieb
iUNets: Fully invertible U-Nets with Learnable Up- and Downsampling. CoRR (2020)
Elena Celledoni, Matthias J. Ehrhardt, Christian Etmann, Robert I. McLachlan, Brynjulf Owren, Carola-Bibiane Schönlieb, Ferdia Sherry
Structure preserving deep learning. CoRR (2020)
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 (2020)
Christian Etmann, Sebastian Lunz, Peter Maass, Carola-Bibiane Schönlieb
On the Connection Between Adversarial Robustness and Saliency Map Interpretability. CoRR (2019)
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 (2019)
Christian Etmann
A Closer Look at Double Backpropagation. CoRR (2019)
Christian Etmann, Sebastian Lunz, Peter Maass, Carola Schönlieb
On the Connection Between Adversarial Robustness and Saliency Map Interpretability. ICML (2019)
Jens Behrmann, Christian Etmann, Tobias Boskamp, Rita Casadonte, Jörg Kriegsmann, Peter Maaß
Deep learning for tumor classification in imaging mass spectrometry. Bioinform. 34 (7) (2018)
Jens Behrmann, Christian Etmann, Tobias Boskamp, Rita Casadonte, Jörg Kriegsmann, Peter Maass
Deep Learning for Tumor Classification in Imaging Mass Spectrometry. CoRR (2017)