
Deep Neural Networks Are Effective At Learning HighDimensional HilbertValued Functions From Limited Data
The accurate approximation of scalarvalued functions from sample points...
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Improved recovery guarantees and sampling strategies for TV minimization in compressive imaging
In this paper, we consider the use of Total Variation (TV) minimization ...
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The benefits of acting locally: Reconstruction algorithms for sparse in levels signals with stable and robust recovery guarantees
The sparsity in levels model recently inspired a new generation of effec...
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The gap between theory and practice in function approximation with deep neural networks
Deep learning (DL) is transforming whole industries as complicated decis...
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The troublesome kernel: why deep learning for inverse problems is typically unstable
There is overwhelming empirical evidence that Deep Learning (DL) leads t...
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Frame approximation with bounded coefficients
Due to their flexibility, frames of Hilbert spaces are attractive altern...
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Optimal sampling strategies for multivariate function approximation on general domains
In this paper, we address the problem of approximating a multivariate fu...
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Do log factors matter? On optimal wavelet approximation and the foundations of compressed sensing
A signature result in compressed sensing is that Gaussian random samplin...
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Uniform recovery in infinitedimensional compressed sensing and applications to structured binary sampling
Infinitedimensional compressed sensing deals with the recovery of analo...
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Convolutional Analysis Operator Learning: Dependence on Training Data
Convolutional analysis operator learning (CAOL) enables the unsupervised...
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On instabilities of deep learning in image reconstruction  Does AI come at a cost?
Deep learning, due to its unprecedented success in tasks such as image c...
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Sparse approximation of multivariate functions from small datasets via weighted orthogonal matching pursuit
We show the potential of greedy recovery strategies for the sparse appro...
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On oracletype local recovery guarantees in compressed sensing
We present improved sampling complexity bounds for stable and robust spa...
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Ben Adcock
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