795 citations · 1.4k across the 64 of their papers we have counts for
5 papers · 1 filter
Near-optimal sampling strategies for multivariate function approximation on general domains
Ben Adcock, Juan M. Cardenas
In this paper, we address the problem of approximating a multivariate function defined on a general domain in dimensions from sample points. We consider weighted least-squares…
Do log factors matter? On optimal wavelet approximation and the foundations of compressed sensing
Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp
A signature result in compressed sensing is that Gaussian random sampling achieves stable and robust recovery of sparse vectors under optimal conditions on the number of measuremen…
Uniform recovery in infinite-dimensional compressed sensing and applications to structured binary sampling
Ben Adcock, Vegard Antun, Anders C. Hansen
Infinite-dimensional compressed sensing deals with the recovery of analog signals (functions) from linear measurements, often in the form of integral transforms such as the Fourier…
On instabilities of deep learning in image reconstruction - Does AI come at a cost?
Vegard Antun, Francesco Renna, Clarice Poon +2
Deep learning, due to its unprecedented success in tasks such as image classification, has emerged as a new tool in image reconstruction with potential to change the field. In this…
Convolutional Analysis Operator Learning: Dependence on Training Data
Il Yong Chun, David Hong, Ben Adcock +1
Convolutional analysis operator learning (CAOL) enables the unsupervised training of (hierarchical) convolutional sparsifying operators or autoencoders from large datasets. One can…