Titles and abstracts are available in the workshop booklet (to appear)

Schedule (ongoing)

Day 1

08:20 – 08:55Welcome
08:55 – 09:05Opening
09:05 – 09:55
Super-resolution in fluorescence microscopy by fluctations of molecules and curve modeling Laure Blanc-Féraud
09:55 – 10:20
Unsupervised Residual Plug-and-Play for Image Super-Resolution with Adaptation to Ultrasound Xuan-Hieu Le
10:20 – 10:45
Self-Supervised deep-learning for reconstruction in digital in-line holographic microscopy Félix Riedel
10:45 – 11:05Coffee Break
11:05 – 11:55
Continuous dictionaries: an introduction and machine learning perspectives Clément Elvira
11:55 – 12:20
Kirchhoff forests for Graph Linear Algebra Simon Barthelmé
12:20 – 13:45Lunch Break
13:45 – 14:35
Unrolled Majorization-Minimization Approaches for Sparse Signal Reconstruction in Analytical Chemistry Émilie Chouzenoux
14:35 – 15:00
LATINO-PRO: LAtent consisTency INverse sOlver with PRompt Optimization Alessio Spagnoletti
15:00 – 15:25
An efficient and guaranteed scheme for posterior sampling in inverse problem based on prior diffusion model Jean-François Giovannelli
15:25 – 15:45Coffee Break
15:45 – 16:35
Opportunities and Challenges for Machine Learning in Positron Emission Tomography Georg Schramm
16:35 – 17:00
Manifold-Aware Langevin Purification for Flow-based Medical Inverse Problem Solvers Antoine De Paepe
17:00 – 17:25
Deep-learning based Plug-And-Play methods for CT reconstruction Idris Tatachak

Day 2

08:30 – 09:00Welcome
09:00 – 09:50
Analytical solutions for CNN inverse problem solvers Pierre Weiss
09:50 – 10:15
Spherical neural fields for dynamic and volumetric harmonic functions Théo Hanon
10:15 – 10:40
On the impact of the parametrization of deep convolutional neural networks on post-training quantization Samy Houache
10:40 – 11:00Coffee Break
11:00 – 11:50
Automated data-driven inverse problem resolution: Applications in microfluidics and epidemiology Barbara Pascal
11:50 – 12:15
Neural network approach for inference of nonlinear mixed effect models based on ordinary differential equations Zhe Li
12:15 – 14:30
Lunch with
Poster session
14:30 – 15:20
Reconstruct Anything Model: a lightweight foundation model for computational imaging Julián Tachella
15:20 – 15:45
Incompressible neural networks with application to image denoising Sébastien Herbreteau
15:45 – 16:05Coffee Break
16:05 – 16:55
Generative AI for medical image reconstruction in positron emission tomography (PET) Andrew Reader
16:55 – 17:15Best Abstract Awards Announcement and Closing