58 citations · 65 across the 10 of their papers we have counts for
13 papers
Variational Augmented Invertible Koopman Autoencoder for probabilistic time series forecasting
Anthony Frion, Lucas Drumetz, Guillaume Tochon +3
Neural Koopman autoencoder models have been shown to successfully build a latent embedding with linear dynamics for arbitrary dynamical systems, enabling strong performance in long…
Augmented Invertible Koopman Autoencoder for long-term time series forecasting
Anthony Frion, Lucas Drumetz, Mauro Dalla Mura +2
Following the introduction of Dynamic Mode Decomposition and its numerous extensions, many neural autoencoder-based implementations of the Koopman operator have recently been propo…
Koopman Ensembles for Probabilistic Time Series Forecasting
Anthony Frion, Lucas Drumetz, Guillaume Tochon +2
In the context of an increasing popularity of data-driven models to represent dynamical systems, many machine learning-based implementations of the Koopman operator have recently b…
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,…
Why is the winner the best?
Matthias Eisenmann, Annika Reinke, Vivienn Weru +122
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to in…
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…
Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021
Carole H. Sudre, Kimberlin Van Wijnen, Florian Dubost +46
Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and…
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
Raghav Mehta, Angelos Filos, Ujjwal Baid +89
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges.…
Going beyond p-convolutions to learn grayscale morphological operators
Alexandre Kirszenberg, Guillaume Tochon, Elodie Puybareau +1
Integrating mathematical morphology operations within deep neural networks has been subject to increasing attention lately. However, replacing standard convolution layers with eros…
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…
Experimental digital Gabor hologram rendering by a model-trained convolutional neural network
J. Rivet, A. Taliercio, C. Fang +4
Digital hologram rendering can be performed by a convolutional neural network, trained with image pairs calculated by numerical wave propagation from sparse generating images. 512-…
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 (…