Picture for Guillaume Tochon

Guillaume Tochon

LRDE

Koopman Ensembles for Probabilistic Time Series Forecasting

Add code
Mar 13, 2024
Viaarxiv icon

Neural Koopman prior for data assimilation

Add code
Sep 11, 2023
Viaarxiv icon

Learning Sentinel-2 reflectance dynamics for data-driven assimilation and forecasting

Add code
May 05, 2023
Viaarxiv icon

Why is the winner the best?

Add code
Mar 30, 2023
Viaarxiv icon

Leveraging Neural Koopman Operators to Learn Continuous Representations of Dynamical Systems from Scarce Data

Add code
Mar 13, 2023
Viaarxiv icon

Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

Add code
Aug 15, 2022
Figure 1 for Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021
Figure 2 for Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021
Figure 3 for Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021
Figure 4 for Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021
Viaarxiv icon

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation -- Analysis of Ranking Metrics and Benchmarking Results

Add code
Dec 19, 2021
Figure 1 for QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation -- Analysis of Ranking Metrics and Benchmarking Results
Figure 2 for QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation -- Analysis of Ranking Metrics and Benchmarking Results
Figure 3 for QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation -- Analysis of Ranking Metrics and Benchmarking Results
Figure 4 for QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation -- Analysis of Ranking Metrics and Benchmarking Results
Viaarxiv icon

Going beyond p-convolutions to learn grayscale morphological operators

Add code
Feb 19, 2021
Figure 1 for Going beyond p-convolutions to learn grayscale morphological operators
Figure 2 for Going beyond p-convolutions to learn grayscale morphological operators
Figure 3 for Going beyond p-convolutions to learn grayscale morphological operators
Figure 4 for Going beyond p-convolutions to learn grayscale morphological operators
Viaarxiv icon