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From Kernel Methods to Neural Networks: A Unifying Variational Formulation

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Updated at: 01-07-2024

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Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions

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Updated at: 13-06-2024

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Sparsest Univariate Learning Models Under Lipschitz Constraint

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Updated at: 13-06-2024

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Physics-Based Neural Network for Non-Invasive Control of Coherent Light in Scattering Media

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Updated at: 13-06-2024

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Learning Weakly Convex Regularizers for Convergent Image-Reconstruction Algorithms

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Updated at: 13-06-2024

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Bayesian Inversion for Nonlinear Imaging Models Using Deep Generative Priors

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Updated at: 13-06-2024

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A Neural-Network-Based Convex Regularizer for Inverse Problems

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Updated at: 13-06-2024

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Deep Learning Meets Sparse Regularization

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Updated at: 13-06-2024

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The Sparsity of Cycle Spinning for Wavelet-Based Solutions of Linear Inverse Problems

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Updated at: 13-06-2024

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Phase Retrieval: From Computational Imaging to Machine Learning: A Tutorial

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Updated at: 13-06-2024

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Dynamic Fourier Ptychography with Deep Spatiotemporal Priors

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Updated at: 13-06-2024

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Learning of Continuous and Piecewise-Linear Functions with Hessian Total-Variation Regularization

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Updated at: 13-06-2024

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A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods

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Updated at: 14-05-2024

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Delaunay-Triangulation-Based Learning with Hessian Total-Variation Regularization

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Updated at: 27-04-2024

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Bayesian Inversion for Nonlinear Imaging Models using Deep Generative Priors

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Updated at: 27-04-2024

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Convex Optimization in Sums of Banach Spaces

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Updated at: 27-04-2024

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Asymptotic Stability in Reservoir Computing

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Updated at: 27-04-2024

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Phase Retrieval: From Computational Imaging to Machine Learning: A tutorial

Project: FunLearn

Updated at: 27-04-2024

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Project acronym FunLearn

Sparsest Univariate Learning Models Under Lipschitz Constraint

Project: FunLearn

Updated at: 27-04-2024

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The Sparsity of Cycle Spinning for Wavelet-Based Solutions of Linear Inverse Problems

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Updated at: 27-04-2024

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Approximation of Lipschitz Functions using Deep Spline Neural Networks

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Updated at: 27-04-2024

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Stability of Image-Reconstruction Algorithms

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Updated at: 27-04-2024

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Physics-based neural network for non-invasive control of coherent light in scattering media

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Updated at: 27-04-2024

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Measuring Complexity of Learning Schemes Using Hessian-Schatten Total Variation

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Updated at: 27-04-2024

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A Constrained Method for Lensless Coherent Imaging of Thin Samples

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Updated at: 27-04-2024

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Project acronym FunLearn

A Neural-Network-Based Convex Regularizer for Inverse Problems

Project: FunLearn

Updated at: 27-04-2024

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Project acronym FunLearn

Dynamic Fourier ptychography with deep spatiotemporal priors

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Updated at: 27-04-2024

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Artifacts in Optical Projection Tomography Due to Refractive-Index Mismatch: Model and Correction

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Updated at: 27-04-2024

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Linear Inverse Problems with Hessian-Schatten Total Variation

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Updated at: 27-04-2024

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Learning of Continuous and Piecewise-Linear Functions With Hessian Total-Variation Regularization

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Updated at: 27-04-2024

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Project acronym FunLearn

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From Kernel Methods to Neural Networks: A Unifying Variational Formulation

Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions

Sparsest Univariate Learning Models Under Lipschitz Constraint

Physics-Based Neural Network for Non-Invasive Control of Coherent Light in Scattering Media

Learning Weakly Convex Regularizers for Convergent Image-Reconstruction Algorithms

Bayesian Inversion for Nonlinear Imaging Models Using Deep Generative Priors

A Neural-Network-Based Convex Regularizer for Inverse Problems

Deep Learning Meets Sparse Regularization

The Sparsity of Cycle Spinning for Wavelet-Based Solutions of Linear Inverse Problems

Phase Retrieval: From Computational Imaging to Machine Learning: A Tutorial

Dynamic Fourier Ptychography with Deep Spatiotemporal Priors

Learning of Continuous and Piecewise-Linear Functions with Hessian Total-Variation Regularization

A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods

Delaunay-Triangulation-Based Learning with Hessian Total-Variation Regularization

Bayesian Inversion for Nonlinear Imaging Models using Deep Generative Priors

Convex Optimization in Sums of Banach Spaces

Asymptotic Stability in Reservoir Computing

Phase Retrieval: From Computational Imaging to Machine Learning: A tutorial

Sparsest Univariate Learning Models Under Lipschitz Constraint

The Sparsity of Cycle Spinning for Wavelet-Based Solutions of Linear Inverse Problems

Approximation of Lipschitz Functions using Deep Spline Neural Networks

Stability of Image-Reconstruction Algorithms

Physics-based neural network for non-invasive control of coherent light in scattering media

Measuring Complexity of Learning Schemes Using Hessian-Schatten Total Variation

A Constrained Method for Lensless Coherent Imaging of Thin Samples

A Neural-Network-Based Convex Regularizer for Inverse Problems

Dynamic Fourier ptychography with deep spatiotemporal priors

Artifacts in Optical Projection Tomography Due to Refractive-Index Mismatch: Model and Correction

Linear Inverse Problems with Hessian-Schatten Total Variation

Learning of Continuous and Piecewise-Linear Functions With Hessian Total-Variation Regularization