353 citations · 1.8k across the 69 of their papers we have counts for
15 papers · 1 filter
Abide by the Law and Follow the Flow: Conservation Laws for Gradient Flows
Sibylle Marcotte, Rémi Gribonval, Gabriel Peyré
Understanding the geometric properties of gradient descent dynamics is a key ingredient in deciphering the recent success of very large machine learning models. A striking observat…
On the Statistical Complexity of Estimation and Testing under Privacy Constraints
Clément Lalanne, Aurélien Garivier, Rémi Gribonval
The challenge of producing accurate statistics while respecting the privacy of the individuals in a sample is an important area of research. We study minimax lower bounds for class…
Compressive Clustering with an Optical Processing Unit
Luc Giffon, Rémi Gribonval
We explore the use of Optical Processing Units (OPU) to compute random Fourier features for sketching, and adapt the overall compressive clustering pipeline to this setting. We als…
Identifiability in Two-Layer Sparse Matrix Factorization
Léon Zheng, Elisa Riccietti, Rémi Gribonval
Sparse matrix factorization is the problem of approximating a matrix by a product of sparse factors .…
Efficient Identification of Butterfly Sparse Matrix Factorizations
Léon Zheng, Elisa Riccietti, Rémi Gribonval
Fast transforms correspond to factorizations of the form , where each factor is sparse and possibly st…
An Embedding of ReLU Networks and an Analysis of their Identifiability
Pierre Stock, Rémi Gribonval
Neural networks with the Rectified Linear Unit (ReLU) nonlinearity are described by a vector of parameters , and realized as a piecewise linear continuous function $R_θ: x \in \…
Statistical Learning Guarantees for Compressive Clustering and Compressive Mixture Modeling
Rémi Gribonval, Gilles Blanchard, Nicolas Keriven +1
We provide statistical learning guarantees for two unsupervised learning tasks in the context of compressive statistical learning, a general framework for resource-efficient large-…
Training with Quantization Noise for Extreme Model Compression
Angela Fan, Pierre Stock, Benjamin Graham +4
We tackle the problem of producing compact models, maximizing their accuracy for a given model size. A standard solution is to train networks with Quantization Aware Training, wher…
Don't take it lightly: Phasing optical random projections with unknown operators
Sidharth Gupta, Rémi Gribonval, Laurent Daudet +1
In this paper we tackle the problem of recovering the phase of complex linear measurements when only magnitude information is available and we control the input. We are motivated b…
Stable safe screening and structured dictionaries for faster L1 regularization
Cassio Fraga Dantas, Rémi Gribonval
In this paper, we propose a way to combine two acceleration techniques for the -regularized least squares problem: safe screening tests, which allow to eliminate useless…
Compressive K-means
Nicolas Keriven, Nicolas Tremblay, Yann Traonmilin +1
The Lloyd-Max algorithm is a classical approach to perform K-means clustering. Unfortunately, its cost becomes prohibitive as the training dataset grows large. We propose a compres…
Flexible Multi-layer Sparse Approximations of Matrices and Applications
Luc Le Magoarou, Rémi Gribonval
The computational cost of many signal processing and machine learning techniques is often dominated by the cost of applying certain linear operators to high-dimensional vectors. Th…
Sparse and spurious: dictionary learning with noise and outliers
Rémi Gribonval, Rodolphe Jenatton, Francis Bach
A popular approach within the signal processing and machine learning communities consists in modelling signals as sparse linear combinations of atoms selected from a learned dictio…
Separable Cosparse Analysis Operator Learning
Matthias Seibert, Julian Wörmann, Rémi Gribonval +1
The ability of having a sparse representation for a certain class of signals has many applications in data analysis, image processing, and other research fields. Among sparse repre…
Learning computationally efficient dictionaries and their implementation as fast transforms
Luc Le Magoarou, Rémi Gribonval
Dictionary learning is a branch of signal processing and machine learning that aims at finding a frame (called dictionary) in which some training data admits a sparse representatio…