Publications related to From regular images to animated heads: a least squares approach

On raw inertial measurements in dynamic networks

Dynamic Networks have been introduced in the literature to solve multi-sensor fusion problems for navigation and mapping. They have been shown to outperform conventional methods in challenging scenarios, such as corridor mapping or self-calibration. In thi ...

2019

Basic gestalt laws cannot explain uncrowding

Michael Herzog, Oh-Hyeon Choung, Einat Rashal

Visual crowding is the inability to perceive elements within clutter. Traditional crowding models, such as pooling, predict that performance deteriorates when flankers are added. However, this prediction has been disproved. For example, performance was fou ...

SAGE PUBLICATIONS LTD2019

Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces

Volkan Cevher, Junhong Lin

We investigate regularized algorithms combining with projection for least-squares regression problem over a Hilbert space, covering nonparametric regression over a reproducing kernel Hilbert space. We prove convergence results with respect to variants of n ...

ICML2018

Reconstruction of 3D scattered data via radial basis functions by efficient and robust techniques

Alberto Crivellaro

We propose new algorithms to overcome two of the most constraining limitations of surface reconstruction methods in use. In particular, we focus on the large amount of data characterizing standard acquisitions by scanner and the noise intrinsically introdu ...

Elsevier Science Bv2017

Case Study of Data Assimilation Methods with the LWR-Proteus Phase II Experimental Campaign

Andreas Pautz, Mathieu Hursin, Daniel Jerôme Siefman

This paper describes the application of data assimilation methods to CASMO-5 simulations of a Proteus research reactor experiment. Its focus is a comparison and evaluation of three prominent data assimilation methods: generalized linear least squares, MOCA ...

2017

Discrete least squares polynomial approximation with random evaluations − application to parametric and stochastic elliptic PDEs

Fabio Nobile, Giovanni Migliorati

Motivated by the numerical treatment of parametric and stochastic PDEs, we analyze the least-squares method for polynomial approximation of multivariate functions based on random sampling according to a given probability measure. Recent work has shown that ...

2015

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

Afsaneh Asaei, Mohammadjavad Taghizadeh, Saeid Haghighatshoar

In this paper, we prove a new identity for the least-square solution of an over-determined set of linear equation

Ax=b

, where

A

is an

m\times n

full-rank matrix,

b

is a column-vector of dimension

m

, and

m

(the number of equations) is larger tha ...

2015

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

Afsaneh Asaei, Mohammadjavad Taghizadeh, Saeid Haghighatshoar

In this paper, we prove a new identity for the least-square solution of an over-determined set of linear equation

Ax=b

, where

A

is an

m\times n

full-rank matrix,

b

is a column-vector of dimension

m

, and

m

(the number of equations) is larger tha ...

Idiap2015

MATHICSE Technical Report : Discrete least squares polynomial approximation with random evaluations – application to parametric and stochastic elliptic PDES

Fabio Nobile, Giovanni Migliorati

Motivated by the numerical treatment of parametric and stochastic PDEs, we analyze the least-squares method for polynomial approximation of multivariate func- tions based on random sampling according to a given probability measure. Recent work has shown th ...

MATHICSE2014

Robust distributed detection over adaptive diffusion networks

Ali H. Sayed, Ali Zoubir

Diffusion adaptation techniques based on the least-mean-squares criterion have been proposed for distributed detection of a signal in Gaussian-distributed noise, forgoing the need for a fusion center. However, least-mean-squares solutions are generally non ...

IEEE2014

From regular images to animated heads: a least squares approach

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On raw inertial measurements in dynamic networks

Basic gestalt laws cannot explain uncrowding

Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces

Reconstruction of 3D scattered data via radial basis functions by efficient and robust techniques

Case Study of Data Assimilation Methods with the LWR-Proteus Phase II Experimental Campaign

Discrete least squares polynomial approximation with random evaluations − application to parametric and stochastic elliptic PDEs

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

MATHICSE Technical Report : Discrete least squares polynomial approximation with random evaluations – application to parametric and stochastic elliptic PDES

Robust distributed detection over adaptive diffusion networks

Reconstruction of 3D scattered data via radial basis functions by efficient and robust techniques

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

Case Study of Data Assimilation Methods with the LWR-Proteus Phase II Experimental Campaign

MATHICSE Technical Report : Discrete least squares polynomial approximation with random evaluations – application to parametric and stochastic elliptic PDES

On raw inertial measurements in dynamic networks

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

Robust distributed detection over adaptive diffusion networks

Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces

Basic gestalt laws cannot explain uncrowding

Discrete least squares polynomial approximation with random evaluations − application to parametric and stochastic elliptic PDEs