Publications related to Sparse dictionary learning

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

Limits on Support Recovery With Probabilistic Models: An Information-Theoretic Framework

Volkan Cevher, Jonathan Mark Scarlett

The support recovery problem consists of determining a sparse subset of a set of variables that is relevant in generating a set of observations, and arises in a diverse range of settings, such as compressive sensing, subset selection in regression, and gro ...

Institute of Electrical and Electronics Engineers2017

Sparse inverse problems for Fourier imaging

Anna Auria Rasclosa

Many natural images have low intrinsic dimension (a.k.a. sparse), meaning that they can be represented with very few coefficients when expressed in an adequate domain. The recent theory of Compressed Sensing exploits this property offering a powerful frame ...

EPFL2017

Face Recognition in Real-world Images

Sabine Süsstrunk, Radhakrishna Achanta, Xavier Philippe Marie Fontaine

Face recognition systems are designed to handle well-aligned images captured under controlled situations. However real-world images present varying orientations, expressions, and illumination conditions. Traditional face recognition algorithms perform poor ...

Ieee2017

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

Efficient Inversion of Multiple-Scattering Model for Optical Diffraction Tomography

Michaël Unser, Thanh-An Michel Pham, Emmanuel Emilien Louis Soubies

Optical diffraction tomography relies on solving an inverse scattering problem governed by the wave equation. Classical reconstruction algorithms are based on linear approximations of the forward model (Born or Rytov), which limits their applicability to t ...

2017

Sparse Pronunciation Codes for Perceptual Phonetic Information Assessment

Hervé Bourlard, Milos Cernak, Afsaneh Asaei, Dhananjay Ram

Speech is a complex signal produced by a highly constrained articulation machinery. Neuro and psycholinguistic theories assert that speech can be decomposed into molecules of structured atoms. Although characterization of the atoms is controversial, the ex ...

2017

Low-rank and Sparse Soft Targets to Learn Better DNN Acoustic Models

Hervé Bourlard, Afsaneh Asaei, Pranay Dighe

Conventional deep neural networks (DNN) for speech acoustic modeling rely on Gaussian mixture models (GMM) and hidden Markov model (HMM) to obtain binary class labels as the targets for DNN training. Subword classes in speech recognition systems correspond ...

Ieee2017

Deep Convolutional Neural Network for Inverse Problems in Imaging

Michaël Unser, Kyong Hwan Jin, Michael Thompson McCann, Emmanuel Augustin Julien Froustey

In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. ...

2017

Subspace Regularized Dynamic Time Warping for Spoken Query Detection

Hervé Bourlard, Afsaneh Asaei, Dhananjay Ram

Deep neural network posterior probabilities are the best features for query detection in speech archives. Dynamic time warping (DTW) is the state-of-the-art solution for this task. Posterior features live in low-dimensional subspaces whereas, the current D ...

2017

Sparse dictionary learning

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An Adaptive Sublinear-Time Block Sparse Fourier Transform

Limits on Support Recovery With Probabilistic Models: An Information-Theoretic Framework

Sparse inverse problems for Fourier imaging

Face Recognition in Real-world Images

Compressive Sampling Using Annihilating Filter-Based Low-Rank Interpolation

Efficient Inversion of Multiple-Scattering Model for Optical Diffraction Tomography

Sparse Pronunciation Codes for Perceptual Phonetic Information Assessment

Low-rank and Sparse Soft Targets to Learn Better DNN Acoustic Models

Deep Convolutional Neural Network for Inverse Problems in Imaging

Subspace Regularized Dynamic Time Warping for Spoken Query Detection

Sparse inverse problems for Fourier imaging

Face Recognition in Real-world Images

Compressive Sampling Using Annihilating Filter-Based Low-Rank Interpolation

An Adaptive Sublinear-Time Block Sparse Fourier Transform

Limits on Support Recovery With Probabilistic Models: An Information-Theoretic Framework

Subspace Regularized Dynamic Time Warping for Spoken Query Detection

Low-rank and Sparse Soft Targets to Learn Better DNN Acoustic Models

Deep Convolutional Neural Network for Inverse Problems in Imaging

Efficient Inversion of Multiple-Scattering Model for Optical Diffraction Tomography

Sparse Pronunciation Codes for Perceptual Phonetic Information Assessment