Publications related to Regularization Networks for Inverse Problems: A state-space approach

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

Optical tomography has been widely investigated for biomedical imaging applications. In recent years, it has been combined with digital holography and has been employed to produce high quality images of phase objects such as cells. In this Thesis, we look ...

EPFL2018

Unlabeled Sensing with Random Linear Measurements

Martin Vetterli, Jayakrishnan Unnikrishnan, Saeid Haghighatshoar

We study the problem of solving a linear sensing system when the observations are unlabeled. Specifically we seek a solution to a linear system of equations y = Ax when the order of the observations in the vector y is unknown. Focusing on the setting in wh ...

Institute of Electrical and Electronics Engineers2018

On the theory of variance reduction for stochastic gradient monte carlo

Nicolas Henri Bernard Flammarion

We show that accelerated gradient descent, averaged gradient descent and the heavy-ball method for quadratic non-strongly-convex problems may be reformulated as constant parameter secondorder difference equation algorithms, where stability of the system is ...

2018

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.

Maryam Kamgarpour, Tyler Summers

We consider the infinite dimensional linear programming (inf-LP) approach for solving stochastic control problems. The inf-LP corresponding to problems with uncountable state and input spaces is in general computationally intractable. By focusing on linear ...

2017

Theory of representation learning in cortical neural networks

Carlos Stein Naves de Brito

Our brain continuously self-organizes to construct and maintain an internal representation of the world based on the information arriving through sensory stimuli. Remarkably, cortical areas related to different sensory modalities appear to share the same f ...

EPFL2016

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

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

Daniel Kressner, Jonas Ballani

We propose a novel combination of the reduced basis method with low-rank tensor techniques for the efficient solution of parameter-dependent linear systems in the case of several parameters. This combination, called rbTensor, consists of three ingredients. ...

Siam Publications2016

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

Daniel Kressner, Jonas Ballani

We propose a novel combination of the reduced basis method with low-rank tensor techniques for the efficient solution of parameter-dependent linear systems in the case of several parameters. This combination, called rb Tensor, consists of three ingredients ...

MATHICSE2015

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

Giancarlo Ferrari Trecate, Marcello Farina

In this paper we consider a linear system structured into physically coupled subsystems and propose a decentralized control scheme capable to guarantee asymptotic stability and satisfaction of constraints on system inputs and states. The design procedure i ...

2013

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

The class of nonlinear systems treated in this technical note consists of the discrete time nonlinear systems that are formed by the interconnection of linear systems through static nonlinearities with few inputs. This special structure is exploited to red ...

Ieee-Inst Electrical Electronics Engineers Inc2012

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

On the theory of variance reduction for stochastic gradient monte carlo

Theory of representation learning in cortical neural networks

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

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

Unlabeled Sensing with Random Linear Measurements

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.

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

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

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