From the 1 of 5 linked papers with an AI index.
5 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…
On the Shift Invariance of Max Pooling Feature Maps in Convolutional Neural Networks
Hubert Leterme, Kévin Polisano, Valérie Perrier +1
This paper focuses on improving the mathematical interpretability of convolutional neural networks (CNNs) in the context of image classification. Specifically, we tackle the instab…
Disentangling Modes and Interference in the Spectrogram of Multicomponent Signals
Kévin Polisano, Sylvain Meignen, Nils Laurent +1
In this paper, we investigate how the spectrogram of multicomponent signals can be decomposed into a mode part and an interference part. We explore two approaches: (i) a variationa…
Distribution-free uncertainty quantification for inverse problems: application to weak lensing mass mapping
Hubert Leterme, Jalal Fadili, Jean-Luc Starck
In inverse problems, distribution-free uncertainty quantification (UQ) aims to obtain error bars with coverage guarantees that are independent of any prior assumptions about the da…
Galaxy-Point Spread Function correlations as a probe of weak-lensing systematics with UNIONS data
Sacha Guerrini, Martin Kilbinger, Hubert Leterme +6
Weak gravitational lensing requires precise measurements of galaxy shapes and therefore an accurate knowledge of the PSF model. The latter can be a source of systematics that affec…