Complex Wavelet Bases, Steerability, and the Marr-Like Pyramid

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Publications associées (29)

H0LiCOW-XI. A weak lensing measurement of the external convergence in the field of the lensed quasar B1608+656 using HST and Subaru deep imaging

Georges Meylan, Frédéric Courbin, Vivien François Bonvin, David Richard Harvey, Olga Tihhonova, Austin Chandler Peel

We investigate the environment and line of sight (LoS) of the H0LiCOW (H-0 Lenses in COSMOGRAIL's Wellspring) lens B1608+656 using Subaru Suprime-Cam and the Hubble Space Telescope (HST) to perform a weak lensing analysis. We compare three different method ...

OXFORD UNIV PRESS2020

Kernel Density Estimation through Density Constrained Near Neighbor Search

Mikhail Kapralov, Navid Nouri

In this paper we revisit the kernel density estimation problem: given a kernel K(x, y) and a dataset of n points in high dimensional Euclidean space, prepare a data structure that can quickly output, given a query q, a (1 + epsilon)-approximation to mu := ...

IEEE2020

Scaling up Kernel Ridge Regression via Locality Sensitive Hashing

Mikhail Kapralov, Amir Zandieh, Navid Nouri

Random binning features, introduced in the seminal paper of Rahimi and Recht '07, are an efficient method for approximating a kernel matrix using locality sensitive hashing. Random binning features provide a very simple and efficient way to approximate the ...

ADDISON-WESLEY PUBL CO2020

Fourier Sampling in Signal Processing and Numerical Linear Algebra

Amir Zandieh

This thesis focuses on developing efficient algorithmic tools for processing large datasets. In many modern data analysis tasks, the sheer volume of available datasets far outstrips our abilities to process them. This scenario commonly arises in tasks incl ...

EPFL2020

Oblivious Sketching of High-Degree Polynomial Kernels

Mikhail Kapralov, Amir Zandieh

Kernel methods are fundamental tools in machine learning that allow detection of non-linear dependencies between data without explicitly constructing feature vectors in high dimensional spaces. A major disadvantage of kernel methods is their poor scalabili ...

ASSOC COMPUTING MACHINERY2020

A comparative study on wavelets and residuals in deep super resolution

Sabine Süsstrunk, Ruofan Zhou, Fayez Lahoud, Majed El Helou

Despite the advances in single-image super resolution using deep convolutional networks, the main problem remains unsolved: recovering fine texture details. Recent works in super resolution aim at modifying the training of neural networks to enable the rec ...

2019

Wavelet variance scale-dependence as a dynamics discriminating tool in high-frequency urban wind speed time series

Fabian Guignard, Dasaraden Mauree

High frequency wind time series measured at different heights from the ground (from 1.5 to 25.5 meters) in an urban area were investigated by using the variance of the coefficients of their wavelet transform. Two ranges of scales were identified, sensitive ...

2019

Sparse Lens Inversion Technique (SLIT): lens and source separability from linear inversion of the source reconstruction problem

Frédéric Courbin, Rémy Elie Joseph

Strong gravitational lensing offers a wealth of astrophysical information on the background source it affects, provided the lensed source can be reconstructed as if it was seen in the absence of lensing. In the present work, we illustrate how sparse optimi ...

EDP SCIENCES S A2019

Learning-Based Image Compression using Convolutional Autoencoder and Wavelet Decomposition

Touradj Ebrahimi, Pinar Akyazi

In this paper, a learning-based image compression method that employs wavelet decomposition as a preprocessing step is presented. The proposed convolutional autoencoder is trained end-to-end to yield a target bitrate smaller than 0.15 bits per pixel across ...

2019

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