181 citations · 702 across the 49 of their papers we have counts for
8 papers · 1 filter
Information-Theoretic Foundations of DNA Data Storage
Ilan Shomorony, Reinhard Heckel
Due to its longevity and enormous information density, DNA is an attractive medium for archival data storage. Thanks to rapid technological advances, DNA storage is becoming practi…
Monotonic Risk Relationships under Distribution Shifts for Regularized Risk Minimization
Daniel LeJeune, Jiayu Liu, Reinhard Heckel
Machine learning systems are often applied to data that is drawn from a different distribution than the training distribution. Recent work has shown that for a variety of classific…
Test-time Recalibration of Conformal Predictors Under Distribution Shift Based on Unlabeled Examples
Fatih Furkan Yilmaz, Reinhard Heckel
Modern image classifiers are very accurate, but the predictions come without uncertainty estimates. Conformal predictors provide uncertainty estimates by computing a set of classes…
Scaling Laws For Deep Learning Based Image Reconstruction
Tobit Klug, Reinhard Heckel
Deep neural networks trained end-to-end to map a measurement of a (noisy) image to a clean image perform excellent for a variety of linear inverse problems. Current methods are onl…
Regularization-wise double descent: Why it occurs and how to eliminate it
Fatih Furkan Yilmaz, Reinhard Heckel
The risk of overparameterized models, in particular deep neural networks, is often double-descent shaped as a function of the model size. Recently, it was shown that the risk as a…
Theoretical Perspectives on Deep Learning Methods in Inverse Problems
Jonathan Scarlett, Reinhard Heckel, Miguel R. D. Rodrigues +2
In recent years, there have been significant advances in the use of deep learning methods in inverse problems such as denoising, compressive sensing, inpainting, and super-resoluti…
Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensing
Mohammad Zalbagi Darestani, Jiayu Liu, Reinhard Heckel
Deep learning based image reconstruction methods outperform traditional methods. However, neural networks suffer from a performance drop when applied to images from a different dis…
Image-to-Image MLP-mixer for Image Reconstruction
Youssef Mansour, Kang Lin, Reinhard Heckel
Neural networks are highly effective tools for image reconstruction problems such as denoising and compressive sensing. To date, neural networks for image reconstruction are almost…