Matching Pursuit with Block Incoherent Dictionaries

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Proportional-type algorithms have attracted much attention because of their fast convergence ability for sparse system identification. To overcome the drawbacks of existing block proportional methods stemming from inadequate block partitioning, this articl ...

Implicit neural representations (INRs) have recently emerged as a promising alternative to classical discretized representations of signals. Nevertheless, despite their practical success, we still do not understand how INRs represent signals. We propose a ...

The goal of this thesis is to study continuous-domain inverse problems for the reconstruction of sparse signals and to develop efficient algorithms to solve such problems computationally. The task is to recover a signal of interest as a continuous function ...

In the last decade, deep neural networks have achieved tremendous success in many fields of machine learning. However, they are shown vulnerable against adversarial attacks: well-designed, yet imperceptible, perturbations can make the state-of-the-art deep ...

Starting from a strong Lattice-Free Maximum Mutual Information (LF-MMI) baseline system, we explore different autoencoder configurations to enhance Mel-Frequency Cepstral Coefficients (MFCC) features. Autoencoders are expected to generate new MFCC features ...

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We consider the problem of distributed representation of signals in sensor networks, where sensors exchange quantized information with their neighbors. The signals of interest are assumed to have a sparse representation with spectral graph dictionaries. We ...

Two popular examples of first-order optimization methods over linear spaces are coordinate descent and matching pursuit algorithms, with their randomized variants. While the former targets the optimization by moving along coordinates, the latter considers ...

In a variety of fields, in particular those involving imaging and optics, we often measure signals whose phase is missing or has been irremediably distorted. Phase retrieval attempts to recover the phase information of a signal from the magnitude of its Fo ...

We characterize the solution of a broad class of convex optimization problems that address the reconstruction of a function from a finite number of linear measurements. The underlying hypothesis is that the solution is decomposable as a finite sum of compo ...

This paper addresses the problem of detecting speech utterances from a large audio archive using a simple spoken query, hence referring to this problem as "Query by Example Spoken Term Detection" (QbE-STD). This still open pattern matching problem has been ...

Compressed sensing is provided a data-acquisition paradigm for sparse signals. Remarkably, it has been shown that the practical algorithms provide robust recovery from noisy linear measurements acquired at a near optimal sampling rate. In many real-world a ...

In this work, we propose a data-driven approach for real-time self-collision avoidance in multi-arm systems. The approach consists of modeling the regions in joint-space that lead to collisions via a Self-Collision Avoidance (SCA) boundary and use it as a ...

We propose a novel sparse dictionary learning method for planar shapes in the sense of Kendall, namely configurations of landmarks in the plane considered up to similitudes. Our shape dictionary method provides a good trade-off between algorithmic simplici ...

Additive models form a widely popular class of regression models which represent the relation between covariates and response variables as the sum of low-dimensional transfer functions. Besides flexibility and accuracy, a key benefit of these models is the ...

We study the Darcy boundary value problem with lognormal permeability field. We adopt a perturbation approach, expanding the solution in Taylor series around the nominal value of the coefficient, and approximating the expected value of the stochastic solut ...

Graph signals offer a very generic and natural representation for data that lives on networks or irregular structures. The actual data structure is however often unknown a priori but can sometimes be estimated from the knowledge of the application domain. ...

We propose a new statistical dictionary learning algorithm for sparse signals that is based on an α-stable innovation model. The parameters of the underlying model—that is, the atoms of the dictionary, the sparsity index α and the dispersion of the transfo ...

SAR and optical imagery provide highly complementary information about observed scenes. A combined use of these two modalities is thus desirable in many data fusion scenarios. However, any data fusion task requires measurements to be accurately aligned. Wh ...

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 ...

Matching Pursuit with Block Incoherent Dictionaries

Chattez avec Graph Search

Design of a Tap-Amplitude-Based Block Proportional Adaptive Filtering Algorithm

A Structured Dictionary Perspective on Implicit Neural Representations

Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks

From Undercomplete to Sparse Overcomplete Autoencoders to Improve LF-MMI Speech Recognition

Sparse and Low-rank Modeling for Automatic Speech Recognition

Learning of robust spectral graph dictionaries for distributed processing

On Matching Pursuit and Coordinate Descent

Super Resolution Phase Retrieval for Sparse Signals

Convex optimization in sums of Banach spaces

Phonetic Subspace Features for Improved Query by Example Spoken Term Detection

Unified Theory for Recovery of Sparse Signals in a General Transform Domain

Multi-Arm Self-Collision Avoidance: A Sparse Solution for a Big Data Problem

Dictionary Learning for Two-Dimensional Kendall Shapes

Multi-task additive models with shared transfer functions based on dictionary learning

Regularity and sparse approximation of the recursive first moment equations for the lognormal Darcy problem

Graph learning under sparsity priors

Dictionary Learning Based on Sparse Distribution Tomography

A deep learning framework for matching of SAR and optical imagery

Sparse inverse problems for Fourier imaging : applications to Optical Interferometry and Diffusion Magnetic Resonance Imaging