
Continuous dictionaries meet lowrank tensor approximations
In this short paper we bridge two seemingly unrelated sparse approximati...
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Sketching Datasets for LargeScale Learning (long version)
This article considers "sketched learning," or "compressive learning," a...
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Sparsitybased audio declipping methods: overview, new algorithms, and largescale evaluation
Recent advances in audio declipping have substantially improved the stat...
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Statistical Learning Guarantees for Compressive Clustering and Compressive Mixture Modeling
We provide statistical learning guarantees for two unsupervised learning...
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Training with Quantization Noise for Extreme Model Compression
We tackle the problem of producing compact models, maximizing their accu...
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Training with Quantization Noise for Extreme FixedPoint Compression
We tackle the problem of producing compact models, maximizing their accu...
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Learning with minibatch Wasserstein : asymptotic and gradient properties
Optimal transport distances are powerful tools to compare probability di...
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And the Bit Goes Down: Revisiting the Quantization of Neural Networks
In this paper, we address the problem of reducing the memory footprint o...
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Don't take it lightly: Phasing optical random projections with unknown operators
In this paper we tackle the problem of recovering the phase of complex l...
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Approximation spaces of deep neural networks
We study the expressivity of deep neural networks. Measuring a network's...
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When does OMP achieve support recovery with continuous dictionaries?
This paper presents new theoretical results on sparse recovery guarantee...
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When does OMP achieves support recovery with continuous dictionaries?
This paper presents new theoretical results on sparse recovery guarantee...
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Equinormalization of Neural Networks
Modern neural networks are overparametrized. In particular, each rectif...
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Stable safe screening and structured dictionaries for faster ℓ_1 regularization
In this paper, we propose a way to combine two acceleration techniques f...
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MULAN: A Blind and OffGrid Method for Multichannel Echo Retrieval
This paper addresses the general problem of blind echo retrieval, i.e., ...
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Concentration of the Frobenius norm of generalized matrix inverses
In many applications it is useful to replace the MoorePenrose pseudoinv...
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Concentration of the Frobenius norms of generalized matrix inverses
In many applications it is useful to replace the MoorePenrose pseudoinv...
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Concentration of the Frobenius norms of pseudoinverses
In many applications it is useful to replace the MoorePenrose pseudoinv...
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On Bayesian Estimation And Proximity Operators
There are two major routes to address the ubiquitous family of inverse p...
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Is the 1norm the best convex sparse regularization?
The 1norm is a good convex regularization for the recovery of sparse ve...
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Instance Optimal Decoding and the Restricted Isometry Property
In this paper, we study the preservation of information in illposed non...
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Learning a Complete Image Indexing Pipeline
To work at scale, a complete image indexing system comprises two compone...
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Sketched Clustering via Hybrid Approximate Message Passing
In sketched clustering, the dataset is first sketched down to a vector o...
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A modeling and algorithmic framework for (non)social (co)sparse audio restoration
We propose a unified modeling and algorithmic framework for audio restor...
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Analyzing the Approximation Error of the Fast Graph Fourier Transform
The graph Fourier transform (GFT) is in general dense and requires O(n^2...
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SUBIC: A supervised, structured binary code for image search
For largescale visual search, highly compressed yet meaningful represen...
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Compressive Statistical Learning with Random Feature Moments
We describe a general framework compressive statistical learning for...
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Compressive Kmeans
The LloydMax algorithm is a classical approach to perform Kmeans clust...
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Approximate search with quantized sparse representations
This paper tackles the task of storing a large collection of vectors, su...
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Sketching for LargeScale Learning of Mixture Models
Learning parameters from voluminous data can be prohibitive in terms of ...
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Compressive Spectral Clustering
Spectral clustering has become a popular technique due to its high perfo...
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Random sampling of bandlimited signals on graphs
We study the problem of sampling kbandlimited signals on graphs. We pro...
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Learning CoSparse Analysis Operators with Separable Structures
In the cosparse analysis model a set of filters is applied to a signal ...
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Dynamic Screening: Accelerating FirstOrder Algorithms for the Lasso and GroupLasso
Recent computational strategies based on screening tests have been propo...
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Sparse and spurious: dictionary learning with noise and outliers
A popular approach within the signal processing and machine learning com...
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On The Sample Complexity of Sparse Dictionary Learning
In the synthesis model signals are represented as a sparse combinations ...
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Sample Complexity of Dictionary Learning and other Matrix Factorizations
Many modern tools in machine learning and signal processing, such as spa...
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Local stability and robustness of sparse dictionary learning in the presence of noise
A popular approach within the signal processing and machine learning com...
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Underdetermined reverberant audio source separation using a fullrank spatial covariance model
This article addresses the modeling of reverberant recording environment...
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Rémi Gribonval
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Directeur de Recherche / Senior Research Scientist at INRIA, Head of the PANAMA projectteam at IRISA, Blaise Pascal Award of the GAMNISMAI by the French Academy of Sciences, and a starting investigator grant from the European Research Council in 2011, IEEE fellow and a EURASIP Fellow, IEEE SPTM Technical Committee and of the SPARS steering committee.