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06/08/2023
Quantum computing algorithms for inverse problems on graphs and an NP-complete inverse problem
We consider an inverse problem for a finite graph (X,E) where we are giv...
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06/06/2023
Globally injective and bijective neural operators
Recently there has been great interest in operator learning, where netwo...
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04/24/2023
A Transfer Principle: Universal Approximators Between Metric Spaces From Euclidean Universal Approximators
We build universal approximators of continuous maps between arbitrary Po...
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10/02/2022
Deep Invertible Approximation of Topologically Rich Maps between Manifolds
How can we design neural networks that allow for stable universal approx...
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03/17/2022
An inverse problem for a semi-linear wave equation: a numerical study
We consider an inverse problem of recovering a potential associated to a...
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10/08/2021
Universal Joint Approximation of Manifolds and Densities by Simple Injective Flows
We analyze neural networks composed of bijective flows and injective exp...
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06/28/2021
Material-separating regularizer for multi-energy X-ray tomography
Dual-energy X-ray tomography is considered in a context where the target...
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06/11/2021
Learning the optimal regularizer for inverse problems
In this work, we consider the linear inverse problem y=Ax+ϵ, where A X→ ...
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02/28/2021
Random tree Besov priors – Towards fractal imaging
We propose alternatives to Bayesian a priori distributions that are freq...
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06/15/2020
Globally Injective ReLU Networks
We study injective ReLU neural networks. Injectivity plays an important ...
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06/02/2020
Deep neural networks for inverse problems with pseudodifferential operators: an application to limited-angle tomography
We propose a novel convolutional neural network (CNN), called ΨDONet, de...
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12/23/2019
Deep learning architectures for nonlinear operator functions and nonlinear inverse problems
We develop a theoretical analysis for special neural network architectur...
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10/11/2019