LAQy: Efficient and Reusable Query Approximations via Lazy Sampling

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Publications associées (27)

Mirrored Langevin Dynamics

Volkan Cevher, Paul Thierry Yves Rolland, Ya-Ping Hsieh, Ali Kavis

We consider the problem of sampling from constrained distributions, which has posed significant challenges to both non-asymptotic analysis and algorithmic design. We propose a unified framework, which is inspired by the classical mirror descent, to derive ...

2018

Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network

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Training robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity. We propose an active learning (AL) framework to select most informative samples ...

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Propagating transport-code input parameter uncertainties with deterministic sampling

Patrick Blanchard, Basil Duval, Ambrogio Fasoli, Yves Martin, Richard Pitts, Olivier Sauter, Henri Weisen, Robin Humphry-Baker, Stefano Coda, Martinus Gijs, Partha Dutta, Marco Wischmeier, Jan Horacek, Vlad Trifa, Yann Camenen, Jonathan Graves, Duccio Testa, Miguel Fernández Ruiz, Sun Hee Kim, Mikhail Maslov, Javier García Hernández, David Pfefferlé, José Pedro Rebelo Ferreira Marques, Julio Rodriguez, Pedro Camilo de Oliveira e Silva, Rebecca Hill, Daniel Scott Alessi, Jonnathan Cesar Hidalgo Acosta, Ana Francisca Leal Silva Soares, Dalziel Joseph Wilson, Pierre-Thomas Paul Brun, Jonathan Marc Philippe Faustin, Federico Nespoli, Alberto Hernando de Castro, Hamish William Patten, Bruno Emanuel Ferreira De Sousa Correia, Li Shuai, Nicola Vianello, Antonio José Pereira de Figueiredo, , , , , , , , ,

Periodic Splines and Gaussian Processes for the Resolution of Linear Inverse Problems

Michaël Unser, Julien René Pierre Fageot, Anaïs Laure Marie-Thérèse Badoual

This paper deals with the resolution of inverse problems in a periodic setting or, in other terms, the reconstruction of periodic continuous-domain signals from their noisy measurements. We focus on two reconstruction paradigms: variational and statistical ...

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Sampling at unknown locations, with an application in surface retrieval

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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 ...

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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

Learning-based subsampling

Volkan Cevher, Ilija Bogunovic, Baran Gözcü, Yen-Huan Li, Luca Baldassarre, Jonathan Mark Scarlett

The present invention concerns a method of sampling a test signal. The method comprises: acquiring (21) training signals sampled at a plurality of sampling locations; running (23) an optimization procedure for determining an index set of n indices, represe ...

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Samuel Lanthaler

Liang Yao

Arnout Lodewijk M Beckers

A novel approach to uncertainty quantification in codes simulating fusion plasma, deterministic sampling (DS), is evaluated. This method uses a few carefully selected samples and can be used to propagate input parameter uncertainties through calculations w ...

2018