181 citations · 702 across the 49 of their papers we have counts for
7 papers · 1 filter
A Provably Convergent Scheme for Compressive Sensing under Random Generative Priors
Wen Huang, Paul Hand, Reinhard Heckel +1
Deep generative modeling has led to new and state of the art approaches for enforcing structural priors in a variety of inverse problems. In contrast to priors given by sparsity, d…
Super-resolution radar imaging via convex optimization
Reinhard Heckel
A radar system emits probing signals and records the reflections. Estimating the relative angles, delays, and Doppler shifts from the received signals allows to determine the locat…
Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks
Reinhard Heckel, Paul Hand
Deep neural networks, in particular convolutional neural networks, have become highly effective tools for compressing images and solving inverse problems including denoising, inpai…
Unsupervised Learning with Stein's Unbiased Risk Estimator
Christopher A. Metzler, Ali Mousavi, Reinhard Heckel +1
Learning from unlabeled and noisy data is one of the grand challenges of machine learning. As such, it has seen a flurry of research with new ideas proposed continuously. In this w…
Rate-Optimal Denoising with Deep Neural Networks
Reinhard Heckel, Wen Huang, Paul Hand +1
Deep neural networks provide state-of-the-art performance for image denoising, where the goal is to recover a near noise-free image from a noisy observation. The underlying princip…
A Characterization of the DNA Data Storage Channel
Reinhard Heckel, Gediminas Mikutis, Robert N. Grass
Owing to its longevity and enormous information density, DNA, the molecule encoding biological information, has emerged as a promising archival storage medium. However, due to tech…
Approximate Ranking from Pairwise Comparisons
Reinhard Heckel, Max Simchowitz, Kannan Ramchandran +1
A common problem in machine learning is to rank a set of n items based on pairwise comparisons. Here ranking refers to partitioning the items into sets of pre-specified sizes accor…