Christian Etmann

According to our database, Christian Etmann authored at least 16 papers between 2018 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Learning Posterior Distributions in Underdetermined Inverse Problems.

Christina Runkel, Michael Möller, Carola-Bibiane Schönlieb, Christian Etmann

Proceedings of the Scale Space and Variational Methods in Computer Vision, 2023

2022

Non-Uniform Diffusion Models.

Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb, Christian Etmann

CoRR, 2022

2021

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans.

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

Nat. Mach. Intell., 2021

Conditional Image Generation with Score-Based Diffusion Models.

Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb, Christian Etmann

CoRR, 2021

CAFLOW: Conditional Autoregressive Flows.

Georgios Batzolis, Marcello Carioni, Christian Etmann, Soroosh Afyouni, Zoe Kourtzi, Carola-Bibiane Schönlieb

CoRR, 2021

Wasserstein GANs Work Because They Fail (to Approximate the Wasserstein Distance).

Jan Stanczuk, Christian Etmann, Lisa Maria Kreusser, Carola-Bibiane Schönlieb

CoRR, 2021

Equivariant neural networks for inverse problems.

Elena Celledoni, Matthias J. Ehrhardt, Christian Etmann, Brynjulf Owren, Carola-Bibiane Schönlieb, Ferdia Sherry

CoRR, 2021

Depthwise Separable Convolutions Allow for Fast and Memory-Efficient Spectral Normalization.

Christina Runkel, Christian Etmann, Michael Möller, Carola-Bibiane Schönlieb

CoRR, 2021

2020

Machine learning for COVID-19 detection and prognostication using chest radiographs and CT scans: a systematic methodological review.

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

CoRR, 2020

Structure preserving deep learning.

Elena Celledoni, Matthias J. Ehrhardt, Christian Etmann, Robert I. McLachlan, Brynjulf Owren, Carola-Bibiane Schönlieb, Ferdia Sherry

CoRR, 2020

iUNets: Fully invertible U-Nets with Learnable Up- and Downsampling.

Christian Etmann, Rihuan Ke, Carola-Bibiane Schönlieb

CoRR, 2020

iUNets: Learnable Invertible Up- and Downsampling for Large-Scale Inverse Problems.

Christian Etmann, Rihuan Ke, Carola-Bibiane Schönlieb

Proceedings of the 30th IEEE International Workshop on Machine Learning for Signal Processing, 2020

2019

Deep Relevance Regularization: Interpretable and Robust Tumor Typing of Imaging Mass Spectrometry Data.

Christian Etmann, Maximilian Schmidt, Jens Behrmann, Tobias Boskamp, Lena Hauberg-Lotte, Annette Peter, Rita Casadonte, Jörg Kriegsmann, Peter Maass

CoRR, 2019

A Closer Look at Double Backpropagation.

Christian Etmann

CoRR, 2019

On the Connection Between Adversarial Robustness and Saliency Map Interpretability.

Christian Etmann, Sebastian Lunz, Peter Maass, Carola Schönlieb

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Deep learning for tumor classification in imaging mass spectrometry.

Jens Behrmann, Christian Etmann, Tobias Boskamp, Rita Casadonte, Jörg Kriegsmann, Peter Maaß

Bioinform., 2018