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Publications

Affichage des publications 811 à 820 sur 1099 au total

Machine Learning

Méthode ensemble de clustering profond

Plusieurs stratégies de classification non supervisée combinant des algorithmes de clustering classiques et les auto-encodeurs ont été étudiées. Ces stratégies améliorent généralement les performances de clustering, mais dépendent des données et des…

Séverine Affeldt, Lazhar Labiod, Mohamed Nadif

Extraction et gestion des connaissances (EGC'2021), -

Numerical Analysis

ZERO: a Local JPEG Grid Origin Detector Based on the Number of DCT Zeros and its Applications in Image Forensics

This work describes a method for detecting JPEG compression as well as its grid origin. The JPEG algorithm performs a quantization of the DCT coefficients of non-overlapping 8×8 blocks of images, setting many of those coefficients to zero. The…

Tina Nikoukhah, Jérémy Anger, Miguel Colom, Jean-Michel Morel, Rafael Grompone von Gioi

Image Processing On Line -

Life Sciences

Cholinesterases in Tripartite Neuromuscular Synapse

The neuromuscular junction (NMJ) is a tripartite synapse in which not only presynaptic and post-synaptic cells participate in synaptic transmission, but also terminal Schwann cells (TSC). Acetylcholine (ACh) is the neurotransmitter that mediates the…

Konstantin Petrov, Svetlana Proskurina, Eric Krejci

Frontiers in Molecular Neuroscience -

Machine Learning

Implicit consensus clustering from multiple graphs

Dealing with relational learning generally relies on tools modeling relational data. An undirected graph can represent these data with vertices depicting entities and edges describing the relationships between the entities. These relationships can…

Rafika Boutalbi, Lazhar Labiod, Mohamed Nadif

Data Mining and Knowledge Discovery -

Signal and Image processing

Tensor Convolutional Dictionary Learning with CP Low-Rank activations

In this paper, we propose an extension of the standard CDL problem with tensor representation, where each activation is constrained to be "low-rank" through a Canonical Polyadic decomposition. We show that this important additional constraint…

Pierre Humbert, Laurent Oudre, Nicolas Vayatis, Julien Audiffren

IEEE Transactions on Signal Processing -

Bioengineering

Adaptive Change-Point Detection for Studying Human Locomotion

This paper presents an innovative method to analyze inertial signals recorded in a semi-controlled environment. It uses an adaptive and supervised change point detection procedure to decompose the signals into homogeneous segments, allowing a…

Sylvain Jung, Laurent Oudre, Charles Truong, Eric Dorveaux, Louis Gorintin, Nicolas Vayatis, Damien Ricard

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) -

Bioengineering

Learning spatial filters from EEG signals with Graph Signal Processing methods

In this paper, we propose to learn a spatial filter directly from Electroencephalography (EEG) signals using graph signal processing tools. We combine a graph learning algorithm with a high-pass graph filter to remove spatially large signals from…

Pierre Humbert, Laurent Oudre, Clément Dubost

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) -

Artificial Intelligence

Learning Laplacian Matrix from Graph Signals with Sparse Spectral Representation

In this paper, we consider the problem of learning a graph structure from multivariate signals, known as graph signals. Such signals are multivariate observations carrying measurements corresponding to the nodes of an unknown graph, which we desire…

Pierre Humbert, Batiste Le Bars, Laurent Oudre, Argyris Kalogeratos, Nicolas Vayatis

Journal of Machine Learning Research -