181 citations · 702 across the 49 of their papers we have counts for
7 papers · 1 filter
Active Sampling Count Sketch (ASCS) for Online Sparse Estimation of a Trillion Scale Covariance Matrix
Zhenwei Dai, Aditya Desai, Reinhard Heckel +1
Estimating and storing the covariance (or correlation) matrix of high-dimensional data is computationally challenging because both memory and computational requirements scale quadr…
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…
Accelerated MRI with Un-trained Neural Networks
Mohammad Zalbagi Darestani, Reinhard Heckel
Convolutional Neural Networks (CNNs) are highly effective for image reconstruction problems. Typically, CNNs are trained on large amounts of training images. Recently, however, un-…
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…
Deep Phase Decoder: Self-calibrating phase microscopy with an untrained deep neural network
Emrah Bostan, Reinhard Heckel, Michael Chen +2
Deep neural networks have emerged as effective tools for computational imaging including quantitative phase microscopy of transparent samples. To reconstruct phase from intensity,…
DNA-Based Storage: Models and Fundamental Limits
Ilan Shomorony, Reinhard Heckel
Due to its longevity and enormous information density, DNA is an attractive medium for archival storage. In this work, we study the fundamental limits and trade-offs of DNA-based s…
Reducing the Representation Error of GAN Image Priors Using the Deep Decoder
Mara Daniels, Paul Hand, Reinhard Heckel
Generative models, such as GANs, learn an explicit low-dimensional representation of a particular class of images, and so they may be used as natural image priors for solving inver…