Research Group Reinhard Heckel
Reinhard Heckel
is Professor for Machine Learning at TU Munich.
His research is centered on machine learning and information processing. He focuses on developing algorithms and theoretical foundations for deep learning, particularly in medical imaging application, and on establishing mathematical and empirical underpinnings for machine learning. Additionally, he works on DNA data storage and the utilization of DNA as a digital information technology.
Team members @MCML
Publications @MCML
[3]
Y. Mansour, X. Zhong, S. Caglar and R. Heckel.
TTT-MIM: Test-Time Training with Masked Image Modeling for Denoising Distribution Shifts.
18th European Conference on Computer Vision (ECCV 2024). Milano, Italy, Sep 29-Oct 04, 2024. To be published.
TTT-MIM: Test-Time Training with Masked Image Modeling for Denoising Distribution Shifts.
18th European Conference on Computer Vision (ECCV 2024). Milano, Italy, Sep 29-Oct 04, 2024. To be published.
MCML Authors
[2]
S. Bamberger, R. Heckel and F. Krahmer.
Approximating Positive Homogeneous Functions with Scale Invariant Neural Networks.
Preprint at arXiv (Aug. 2023). arXiv.
Approximating Positive Homogeneous Functions with Scale Invariant Neural Networks.
Preprint at arXiv (Aug. 2023). arXiv.
MCML Authors
[1]
Y. Mansour and R. Heckel.
Zero-Shot Noise2Noise: Efficient Image Denoising without any Data.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023). Vancouver, Canada, Jun 18-23, 2023. DOI.
Zero-Shot Noise2Noise: Efficient Image Denoising without any Data.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023). Vancouver, Canada, Jun 18-23, 2023. DOI.