64 citations · 244 across the 14 of their papers we have counts for
26 papers
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…
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…
Data augmentation for deep learning based accelerated MRI reconstruction with limited data
Zalan Fabian, Reinhard Heckel, Mahdi Soltanolkotabi
Deep neural networks have emerged as very successful tools for image restoration and reconstruction tasks. These networks are often trained end-to-end to directly reconstruct an im…
Measuring Robustness in Deep Learning Based Compressive Sensing
Mohammad Zalbagi Darestani, Akshay S. Chaudhari, Reinhard Heckel
Deep neural networks give state-of-the-art accuracy for reconstructing images from few and noisy measurements, a problem arising for example in accelerated magnetic resonance imagi…
Early Stopping in Deep Networks: Double Descent and How to Eliminate it
Reinhard Heckel, Fatih Furkan Yilmaz
Over-parameterized models, such as large deep networks, often exhibit a double descent phenomenon, whereas a function of model size, error first decreases, increases, and decreases…
Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximation
Reinhard Heckel, Mahdi Soltanolkotabi
Un-trained convolutional neural networks have emerged as highly successful tools for image recovery and restoration. They are capable of solving standard inverse problems such as d…