181 citations · 702 across the 49 of their papers we have counts for
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Provable Continual Learning via Sketched Jacobian Approximations
Reinhard Heckel
An important problem in machine learning is the ability to learn tasks in a sequential manner. If trained with standard first-order methods most models forget previously learned ta…
Achieving the Capacity of a DNA Storage Channel with Linear Coding Schemes
Kel Levick, Reinhard Heckel, Ilan Shomorony
Due to the redundant nature of DNA synthesis and sequencing technologies, a basic model for a DNA storage system is a multi-draw "shuffling-sampling" channel. In this model, a rand…
Untrained Graph Neural Networks for Denoising
Samuel Rey, Santiago Segarra, Reinhard Heckel +1
A fundamental problem in signal processing is to denoise a signal. While there are many well-performing methods for denoising signals defined on regular supports, such as images de…
Interpolation can hurt robust generalization even when there is no noise
Konstantin Donhauser, Alexandru Ţifrea, Michael Aerni +2
Numerous recent works show that overparameterization implicitly reduces variance for min-norm interpolators and max-margin classifiers. These findings suggest that ridge regulariza…
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…