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

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Related publications (30)

Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections

We study the least-squares regression problem over a Hilbert space, covering nonparametric regression over a reproducing kernel Hilbert space as a special case. We rst investigate regularized algorithms adapted to a projection operator on a closed subspace ...

2020

Coordinate Difference Matrices

Martin Vetterli, Gilles Baechler, Golnooshsadat Elhami, Miranda Krekovic

In many problems such as phase retrieval, molecular biology, source localization, and sensor array calibration, one can measure vector differences between pairs of points and attempt to recover the position of these points; this class of problems is called ...

SIAM PUBLICATIONS2020

Shape Reconstruction by Learning Differentiable Surface Representations

Pascal Fua, Mathieu Salzmann, Erhan Gündogdu, Shaifali Parashar, Jan Bednarík

Generative models that produce point clouds have emerged as a powerful tool to represent 3D surfaces, and the best current ones rely on learning an ensemble of parametric representations. Unfortunately, they offer no control over the deformations of the su ...

IEEE2020

Walkman: A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization

Ali H. Sayed, Kun Yuan

This paper introduces a new algorithm for consensus optimization in a multi-agent network, where all agents collaboratively find a minimizer for the sum of their private functions. All decentralized algorithms rely on communications between adjacent nodes. ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

On raw inertial measurements in dynamic networks

Jan Skaloud, Davide Antonio Cucci

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

Performance Assessment of Kron Reduction in the Numerical Analysis of Polyphase Power Systems

Mario Paolone, Andreas Martin Kettner

This paper investigates the impact of Kron reduction on the performance of numerical methods applied to the analysis of unbalanced polyphase power systems. Specifically, this paper focuses on power-flow study, state estimation, and voltage stability assess ...

IEEE2019

A new analysis approach for T-2 relaxometry myelin water quantification: Orthogonal Matching Pursuit

Guillaume Jean Op 't Veld

Purpose: In vivo myelin quantification can provide valuable noninvasive information on neuronal maturation and development, as well as insights into neurological disorders. Multiexponential analysis of multiecho T-2 relaxation is a powerful and widely appl ...

Wiley2019

Robust Biophysical Parameter Estimation with a Neural Network Enhanced Hamiltonian Markov Chain Monte Carlo Sampler

Jean-Philippe Thiran, Gabriel Girard, Jonathan Rafael Patino Lopez, Erick Jorge Canales Rodriguez, Marco Pizzolato, Thomas Yu

Probabilistic parameter estimation in model fitting runs the gamut from maximum likelihood or maximum a posteriori point estimates from optimization to Markov Chain Monte Carlo (MCMC) sampling. The latter, while more computationally intensive, generally pr ...

SPRINGER INTERNATIONAL PUBLISHING AG2019

Robust Adaptive Decision Making: Bayesian Optimization and Beyond

Ilija Bogunovic

The central task in many interactive machine learning systems can be formalized as the sequential optimization of a black-box function. Bayesian optimization (BO) is a powerful model-based framework for \emph{adaptive} experimentation, where the primary go ...

EPFL2019

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

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