Recent advances in the Internet of Things (IoT) are giving rise to a proliferation of
interconnected devices, allowing the use of various smart applications. The enormous …

As computational biologists continue to be inundated by ever increasing amounts of
metagenomic data, the need for data analysis approaches that keep up with the pace of …

We propose Intermediate Layer Optimization (ILO), a novel optimization algorithm for solving
inverse problems with deep generative models. Instead of optimizing only over the initial …

Given an inverse problem with a normalizing flow prior, we wish to estimate the distribution
of the underlying signal conditioned on the observations. We approach this problem as a …

Abstract A Generative Adversarial Network (GAN) with generator G trained to model the prior
of images has been shown to perform better than sparsity-based regularizers in ill-posed …

Linear encoding of sparse vectors is widely popular, but is commonly data-independent–
missing any possible extra (but a priori unknown) structure beyond sparsity. In this paper we …

Hyperspectral sensors acquire spectral responses from objects with a large number of
narrow spectral bands. The large volume of data may be costly in terms of storage and …

The field of deep learning is commonly concerned with optimizing predictive models using
large pre-acquired datasets of densely sampled datapoints or signals. In this work, we …

Recently, deep network-based image compressed sensing methods achieved high
reconstruction quality and reduced computational overhead compared with traditional …

Sparse support recovery arises in many applications in communications and signal
processing. Existing methods tackle sparse support recovery problems for a given …