Publications associées à Regularization Networks for Inverse Problems: A state-space approach

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Representer Theorems for Sparsity-Promoting $ ℓ _{ 1 } $ Regularization

Michaël Unser, Julien René Pierre Fageot, Harshit Gupta

We present a theoretical analysis and comparison of the effect of

ℓ _{ 1 }

versus

ℓ _{ 2 }

regularization for the resolution of ill-posed linear inverse and/or compressed sensing problems. Our formulation covers the most general setting where the s ...

IEEE2016

Regularization Networks for Inverse Problems: A state-space approach

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3D Reconstruction of Optical Diffraction Tomography Based on a Neural Network Model

Unlabeled Sensing with Random Linear Measurements

On the theory of variance reduction for stochastic gradient monte carlo

On infinite dimensional linear programming approach to stochastic control * *This research is partially supported by M. Kamgarpour’s European Union ERC Starting Grant, CONENE and by T. Summers’ the US National Science Foundation under grant CNS-1566127.

Theory of representation learning in cortical neural networks

Representer Theorems for Sparsity-Promoting $ ℓ _{ 1 } $ Regularization

Reduced Basis Methods: From Low-Rank Matrices To Low-Rank Tensors

MATHICSE Technical Report: Reduced basis methods: from low-rank matrices to low-rank tensor

Plug-and-Play Decentralized Model Predictive Control for Linear Systems

Kalman Filtering for a Generalized Class of Nonlinear Systems and a New Gaussian Quadrature Technique

Unlabeled Sensing with Random Linear Measurements

3D Reconstruction of Optical Diffraction Tomography Based on a Neural Network Model

On the theory of variance reduction for stochastic gradient monte carlo

Plug-and-Play Decentralized Model Predictive Control for Linear Systems

MATHICSE Technical Report: Reduced basis methods: from low-rank matrices to low-rank tensor

Reduced Basis Methods: From Low-Rank Matrices To Low-Rank Tensors

On infinite dimensional linear programming approach to stochastic control * *This research is partially supported by M. Kamgarpour’s European Union ERC Starting Grant, CONENE and by T. Summers’ the US National Science Foundation under grant CNS-1566127.

Theory of representation learning in cortical neural networks

Representer Theorems for Sparsity-Promoting $ ℓ _{ 1 } $ Regularization

Kalman Filtering for a Generalized Class of Nonlinear Systems and a New Gaussian Quadrature Technique