From the 1 of 6 linked papers with an AI index.
1 citations · 1 across the 3 of their papers we have counts for
6 papers
A plug-and-play approach with fast uncertainty quantification for weak lensing mass mapping
Hubert Leterme, Andreas Tersenov, Jalal Fadili +1
The paper presents PnPMass, a plug‑and‑play algorithm that reconstructs dark‑matter maps from weak‑lensing shear data using a single deep‑learning denoiser combined with gradient d…
Carleman Estimates and Controllability of Stochastic degenerate parabolic Heat Equations
M. Baroun, M. Fadili, A. Khchine +1
This paper concerns the null controllability for a class of stochastic degenerate parabolic equations. We first establish a global Carleman estimate for a linear forward stochastic…
A New Perspective on Precision and Recall for Generative Models
Benjamin Sykes, Loïc Simon, Julien Rabin +1
With the recent success of generative models in image and text, the question of their evaluation has recently gained a lot of attention. While most methods from the state of the ar…
Neutrino Oscillation Parameter Estimation Using Structured Hierarchical Transformers
Giorgio Morales, Gregory Lehaut, Antonin Vacheret +2
Neutrino oscillations encode fundamental information about neutrino masses and mixing parameters, offering a unique window into physics beyond the Standard Model. Estimating these…
Towards Uncertainty Quantification in Generative Model Learning
Giorgio Morales, Frederic Jurie, Jalal Fadili
While generative models have become increasingly prevalent across various domains, fundamental concerns regarding their reliability persist. A crucial yet understudied aspect of th…
Learning-to-Optimize with PAC-Bayesian Guarantees: Theoretical Considerations and Practical Implementation
Michael Sucker, Jalal Fadili, Peter Ochs
We use the PAC-Bayesian theory for the setting of learning-to-optimize. To the best of our knowledge, we present the first framework to learn optimization algorithms with provable…