78 citations · 86 across the 6 of their papers we have counts for
6 papers
Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks
Ben Adcock, Simone Brugiapaglia, Nick Dexter +1
Learning approximations to smooth target functions of many variables from finite sets of pointwise samples is an important task in scientific computing and its many applications in…
CAS4DL: Christoffel Adaptive Sampling for function approximation via Deep Learning
Ben Adcock, Juan M. Cardenas, Nick Dexter
The problem of approximating smooth, multivariate functions from sample points arises in many applications in scientific computing, e.g., in computational Uncertainty Quantificatio…
Analyzing the structure of multidimensional compressed sensing problems through coherence
Alex Jones, Ben Adcock, Anders Hansen
Recently it has been established that asymptotic incoherence can be used to facilitate subsampling, in order to optimize reconstruction quality, in a variety of continuous compress…
On asymptotic structure in compressed sensing
Bogdan Roman, Anders Hansen, Ben Adcock
This paper demonstrates how new principles of compressed sensing, namely asymptotic incoherence, asymptotic sparsity and multilevel sampling, can be utilised to better understand u…
The quest for optimal sampling: Computationally efficient, structure-exploiting measurements for compressed sensing
Ben Adcock, Anders C. Hansen, Bogdan Roman
An intriguing phenomenon in many instances of compressed sensing is that the reconstruction quality is governed not just by the overall sparsity of the signal, but also on its stru…
Linear Stable Sampling Rate: Optimality of 2D Wavelet Reconstructions from Fourier Measurements
Ben Adcock, Anders C. Hansen, Gitta Kutyniok +1
In this paper we analyze two-dimensional wavelet reconstructions from Fourier samples within the framework of generalized sampling. For this, we consider both separable compactly-s…