260 citations · 657 across the 30 of their papers we have counts for
31 papers
MultiHU-TD: Multifeature Hyperspectral Unmixing Based on Tensor Decomposition
Mohamad Jouni, Mauro Dalla Mura, Lucas Drumetz +1
Hyperspectral unmixing allows representing mixed pixels as a set of pure materials weighted by their abundances. Spectral features alone are often insufficient, so it is common to…
Neural Koopman prior for data assimilation
Anthony Frion, Lucas Drumetz, Mauro Dalla Mura +2
With the increasing availability of large scale datasets, computational power and tools like automatic differentiation and expressive neural network architectures, sequential data…
Learning Sentinel-2 reflectance dynamics for data-driven assimilation and forecasting
Anthony Frion, Lucas Drumetz, Guillaume Tochon +2
Over the last few years, massive amounts of satellite multispectral and hyperspectral images covering the Earth's surface have been made publicly available for scientific purpose,…
Leveraging Neural Koopman Operators to Learn Continuous Representations of Dynamical Systems from Scarce Data
Anthony Frion, Lucas Drumetz, Mauro Dalla Mura +2
Over the last few years, several works have proposed deep learning architectures to learn dynamical systems from observation data with no or little knowledge of the underlying phys…
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals
Clément Bonet, Benoît Malézieux, Alain Rakotomamonjy +4
When dealing with electro or magnetoencephalography records, many supervised prediction tasks are solved by working with covariance matrices to summarize the signals. Learning with…
Disambiguation of One-Shot Visual Classification Tasks: A Simplex-Based Approach
Yassir Bendou, Lucas Drumetz, Vincent Gripon +2
The field of visual few-shot classification aims at transferring the state-of-the-art performance of deep learning visual systems onto tasks where only a very limited number of tra…
Hyperbolic Sliced-Wasserstein via Geodesic and Horospherical Projections
Clément Bonet, Laetitia Chapel, Lucas Drumetz +1
It has been shown beneficial for many types of data which present an underlying hierarchical structure to be embedded in hyperbolic spaces. Consequently, many tools of machine lear…
Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities
Yassine El Ouahidi, Lucas Drumetz, Giulia Lioi +3
BCI Motor Imagery datasets usually are small and have different electrodes setups. When training a Deep Neural Network, one may want to capitalize on all these datasets to increase…
Geometry-preserving Lie Group Integrators For Differential Equations On The Manifold Of Symmetric Positive Definite Matrices
Lucas Drumetz, Alexandre Reiffers-Masson, Naoufal El Bekri +1
In many applications, one encounters signals that lie on manifolds rather than a Euclidean space. In particular, covariance matrices are examples of ubiquitous mathematical objects…
Active Few-Shot Classification: a New Paradigm for Data-Scarce Learning Settings
Aymane Abdali, Vincent Gripon, Lucas Drumetz +1
We consider a novel formulation of the problem of Active Few-Shot Classification (AFSC) where the objective is to classify a small, initially unlabeled, dataset given a very restra…
Turning Normalizing Flows into Monge Maps with Geodesic Gaussian Preserving Flows
Guillaume Morel, Lucas Drumetz, Simon Benaïchouche +2
Normalizing Flows (NF) are powerful likelihood-based generative models that are able to trade off between expressivity and tractability to model complex densities. A now well estab…
Preserving Fine-Grain Feature Information in Classification via Entropic Regularization
Raphael Baena, Lucas Drumetz, Vincent Gripon
Labeling a classification dataset implies to define classes and associated coarse labels, that may approximate a smoother and more complicated ground truth. For example, natural im…
Spherical Sliced-Wasserstein
Clément Bonet, Paul Berg, Nicolas Courty +3
Many variants of the Wasserstein distance have been introduced to reduce its original computational burden. In particular the Sliced-Wasserstein distance (SW), which leverages one-…
Preventing Manifold Intrusion with Locality: Local Mixup
Raphael Baena, Lucas Drumetz, Vincent Gripon
Mixup is a data-dependent regularization technique that consists in linearly interpolating input samples and associated outputs. It has been shown to improve accuracy when used to…
Graphs as Tools to Improve Deep Learning Methods
Carlos Lassance, Myriam Bontonou, Mounia Hamidouche +3
In recent years, deep neural networks (DNNs) have known an important rise in popularity. However, although they are state-of-the-art in many machine learning challenges, they still…
Learning stochastic dynamical systems with neural networks mimicking the Euler-Maruyama scheme
Noura Dridi, Lucas Drumetz, Ronan Fablet
Stochastic differential equations (SDEs) are one of the most important representations of dynamical systems. They are notable for the ability to include a deterministic component o…
Inferring Graph Signal Translations as Invariant Transformations for Classification Tasks
Raphael Baena, Lucas Drumetz, Vincent Gripon
The field of Graph Signal Processing (GSP) has proposed tools to generalize harmonic analysis to complex domains represented through graphs. Among these tools are translations, whi…
Improving Classification Accuracy with Graph Filtering
Mounia Hamidouche, Carlos Lassance, Yuqing Hu +3
In machine learning, classifiers are typically susceptible to noise in the training data. In this work, we aim at reducing intra-class noise with the help of graph filtering to imp…
Learning Sentinel-2 Spectral Dynamics for Long-Run Predictions Using Residual Neural Networks
Joaquim Estopinan, Guillaume Tochon, Lucas Drumetz
Making the most of multispectral image time-series is a promising but still relatively under-explored research direction because of the complexity of jointly analyzing spatial, spe…
Variational Deep Learning for the Identification and Reconstruction of Chaotic and Stochastic Dynamical Systems from Noisy and Partial Observations
Duong Nguyen, Said Ouala, Lucas Drumetz +1
The data-driven recovery of the unknown governing equations of dynamical systems has recently received an increasing interest. However, the identification of governing equations re…
Learning Variational Data Assimilation Models and Solvers
Ronan Fablet, Bertrand Chapron, Lucas. Drumetz +3
This paper addresses variational data assimilation from a learning point of view. Data assimilation aims to reconstruct the time evolution of some state given a series of observati…
Joint learning of variational representations and solvers for inverse problems with partially-observed data
Ronan Fablet, Lucas Drumetz, Francois Rousseau
Designing appropriate variational regularization schemes is a crucial part of solving inverse problems, making them better-posed and guaranteeing that the solution of the associate…
Filtering Internal Tides From Wide-Swath Altimeter Data Using Convolutional Neural Networks
Redouane Lguensat, Ronan Fablet, Julien Le Sommer +5
The upcoming Surface Water Ocean Topography (SWOT) satellite altimetry mission is expected to yield two-dimensional high-resolution measurements of Sea Surface Height (SSH), thus a…
Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review
Ricardo Augusto Borsoi, Tales Imbiriba, José Carlos Moreira Bermudez +6
The spectral signatures of the materials contained in hyperspectral images, also called endmembers (EM), can be significantly affected by variations in atmospheric, illumination or…