Publications related to Sampling innovations

Sampling at unknown locations, with an application in surface retrieval

Martin Vetterli, Benjamin Bejar Haro, Adam James Scholefield, Michalina Wanda Pacholska

We consider the problem of sampling at unknown locations. We prove that, in this setting, if we take arbitrarily many samples of a polynomial or real bandlimited signal, it is possible to find another function in the same class, arbitrarily far away from t ...

2017

Safe Adaptive Importance Sampling

Martin Jaggi, Sebastian Urban Stich, Anant Raj

Importance sampling has become an indispensable strategy to speed up optimization algorithms for large-scale applications. Improved adaptive variants - using importance values defined by the complete gradient information which changes during optimization - ...

2017

Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Fabio Nobile, Giovanni Migliorati

We analyze the accuracy of the discrete least-squares approximation of a function

u

in multivariate polynomial spaces

P_\Lambda:=span\{y\mapsto y^\nu \,: \, \nu\in \Lambda\}

with

\Lambda\subset N_0^d

over the domain

\Gamma:=[-1,1]^d

, based on the s ...

2017

Sampling and Exact Reconstruction of Pulses with Variable Width

Martin Vetterli, Adam James Scholefield, Loïc Arnaud Baboulaz, Gilles Baechler

Recent sampling results enable the reconstruction of signals composed of streams of fixed-shaped pulses. These results have found applications in topics as varied as channel estimation, biomedical imaging and radio astronomy. However, in many real signals, ...

Institute of Electrical and Electronics Engineers2017

A Sampling Theory for Non-Decaying Signals

Michaël Unser, Quy Ha Nguyen

The classical assumption in sampling and spline theories is that the input signal is square-integrable, which prevents us from applying such techniques to signals that do not decay or even grow at infinity. In this paper, we develop a sampling theory for m ...

2017

Media item relighting technique

Martin Vetterli, Loïc Arnaud Baboulaz, Damien Firmenich, Niranjan Thanikachalam

The present invention concerns a method of method of relighting a media item comprising media elements. The method comprises, for at least some of the media elements: determining, in a first signal domain, a light transport function describing the appearan ...

2017

Compressive Sampling Using Annihilating Filter-Based Low-Rank Interpolation

Kyong Hwan Jin

While the recent theory of compressed sensing provides an opportunity to overcome the Nyquist limit in recovering sparse signals, a solution approach usually takes the form of an inverse problem of an unknown signal, which is crucially dependent on specifi ...

IEEE2017

An Adaptive Sublinear-Time Block Sparse Fourier Transform

Volkan Cevher, Mikhail Kapralov, Jonathan Mark Scarlett, Amir Zandieh

The problem of approximately computing the

k

dominant Fourier coefficients of a vector

X

quickly, and using few samples in time domain, is known as the Sparse Fourier Transform (sparse FFT) problem. A long line of work on the sparse FFT has resulted in ...

2017

Multilevel weighted least squares polynomial approximation

Fabio Nobile

Weighted least squares polynomial approximation uses random samples to determine projections of functions onto spaces of polynomials. It has been shown that, using an optimal distribution of sample locations, the number of samples required to achieve quasi ...

2017

The Particle-In-Fourier (PIF) Approach Applied to Gyrokinetic Simulations

Laurent Villard, Stephan Brunner, Emmanuel Lanti, Noé Thomas Elie Ohana, Claudio Gheller, Aaron Lewis Scheinberg

A widespread method for gyrokinetic simulations relies on the discretization of distribution functions with numerical markers. The electromagnetic field is typically represented on a grid by finite differences or finite elements (Particle-In-Cell, PIC). Wh ...

2017

Sampling innovations

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Sampling at unknown locations, with an application in surface retrieval

Safe Adaptive Importance Sampling

Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Sampling and Exact Reconstruction of Pulses with Variable Width

A Sampling Theory for Non-Decaying Signals

Media item relighting technique

Compressive Sampling Using Annihilating Filter-Based Low-Rank Interpolation

An Adaptive Sublinear-Time Block Sparse Fourier Transform

Multilevel weighted least squares polynomial approximation

The Particle-In-Fourier (PIF) Approach Applied to Gyrokinetic Simulations

Sampling at unknown locations, with an application in surface retrieval

Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Sampling and Exact Reconstruction of Pulses with Variable Width

The Particle-In-Fourier (PIF) Approach Applied to Gyrokinetic Simulations

Media item relighting technique

Safe Adaptive Importance Sampling

An Adaptive Sublinear-Time Block Sparse Fourier Transform

A Sampling Theory for Non-Decaying Signals

Compressive Sampling Using Annihilating Filter-Based Low-Rank Interpolation

Multilevel weighted least squares polynomial approximation