24 citations · 51 across the 6 of their papers we have counts for
6 papers
Detection of Abnormal Vessel Behaviours from AIS data using GeoTrackNet: from the Laboratory to the Ocean
Duong Nguyen, Matthieu Simonin, Guillaume Hajduch +3
The constant growth of maritime traffic leads to the need of automatic anomaly detection, which has been attracting great research attention. Information provided by AIS (Automatic…
Learning Endmember Dynamics in Multitemporal Hyperspectral Data Using a State-Space Model Formulation
Lucas Drumetz, Mauro Dalla Mura, Guillaume Tochon +1
Hyperspectral image unmixing is an inverse problem aiming at recovering the spectral signatures of pure materials of interest (called endmembers) and estimating their proportions (…
Learning Generalized Quasi-Geostrophic Models Using Deep Neural Numerical Models
Redouane Lguensat, Julien Le Sommer, Sammy Metref +2
We introduce a new strategy designed to help physicists discover hidden laws governing dynamical systems. We propose to use machine learning automatic differentiation libraries to…
End-to-end learning of energy-based representations for irregularly-sampled signals and images
Ronan Fablet, Lucas Drumetz, François Rousseau
For numerous domains, including for instance earth observation, medical imaging, astrophysics,..., available image and signal datasets often involve irregular space-time sampling p…
EM-like Learning Chaotic Dynamics from Noisy and Partial Observations
Duong Nguyen, Said Ouala, Lucas Drumetz +1
The identification of the governing equations of chaotic dynamical systems from data has recently emerged as a hot topic. While the seminal work by Brunton et al. reported proof-of…
Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection
Duong Nguyen, Oliver S. Kirsebom, Fábio Frazão +2
In this paper, we adapt Recurrent Neural Networks with Stochastic Layers, which are the state-of-the-art for generating text, music and speech, to the problem of acoustic novelty d…