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03/27/2023
Manifold Learning by Mixture Models of VAEs for Inverse Problems
Representing a manifold of very high-dimensional data with generative mo...
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02/07/2023
Compressed sensing for inverse problems and the sample complexity of the sparse Radon transform
Compressed sensing allows for the recovery of sparse signals from few me...
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05/29/2022
Continuous Generative Neural Networks
In this work, we present and study Continuous Generative Neural Networks...
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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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11/05/2020
Neural networks for classification of strokes in electrical impedance tomography on a 3D head model
We consider the problem of the detection of brain hemorrhages from three...
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10/16/2020
Compressed sensing photoacoustic tomography reduces to compressed sensing for undersampled Fourier measurements
Photoacoustic tomography (PAT) is an emerging imaging modality that aims...
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09/17/2019