Tobías I. Liaudat

13 papers Journal 12
YearRankTypeTitle / Venue / Authors
2026 J jnl
Deep unfolding of MCMC kernels: scalable, modular & explainable GANs for high-dimensional posterior sampling.
CoRR
Jonathan Spence, Tobías I. Liaudat, Konstantinos Zygalakis, Marcelo Pereyra
2025 J jnl
Bayesian model selection and misspecification testing in imaging inverse problems only from noisy and partial measurements.
CoRR
Tom Sprunck, Marcelo Pereyra, Tobías I. Liaudat
2025 J jnl
DeepInverse: A Python package for solving imaging inverse problems with deep learning.
J. Open Source Softw.
Julián Tachella, Matthieu Terris, Samuel Hurault, Andrew Wang, Leo Davy, Jérémy Scanvic, Victor Sechaud, Romain Vo, Thomas Moreau, Thomas Davies, Dongdong Chen, Nils Laurent, Brayan Monroy, Jonathan Dong, Zhiyuan Hu, Minh-Hai Nguyen, Florian Sarron, Pierre Weiss, Paul Escande, Mathurin Massias, Thibaut Modrzyk, Brett Levac, Tobías I. Liaudat, Maxime Song, Johannes Hertrich, Sebastian Neumayer, Georg Schramm
2025 J jnl
Generative imaging for radio interferometry with fast uncertainty quantification.
CoRR
Matthijs Mars, Tobías I. Liaudat, Jessica J. Whitney, Marta M. Betcke, Jason D. McEwen
2025 J jnl
LoFi: Neural Local Fields for Scalable Image Reconstruction.
IEEE Trans. Computational Imaging
AmirEhsan Khorashadizadeh, Tobías I. Liaudat, Tianlin Liu, Jason D. McEwen, Ivan Dokmanic
2024 J jnl
LoFi: Scalable Local Image Reconstruction with Implicit Neural Representation.
CoRR
AmirEhsan Khorashadizadeh, Tobías I. Liaudat, Tianlin Liu, Jason D. McEwen, Ivan Dokmanic
2024 J jnl
Uncertainty quantification for fast reconstruction methods using augmented equivariant bootstrap: Application to radio interferometry.
CoRR
Mostafa Cherif, Tobías I. Liaudat, Jonathan Kern, Christophe Kervazo, Jérôme Bobin
2023 J jnl
Point spread function modelling for astronomical telescopes: a review focused on weak gravitational lensing studies.
CoRR
Tobías I. Liaudat, Jean-Luc Starck, Martin Kilbinger, Pierre-Antoine Frugier
2023 J jnl
Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging.
CoRR
Tobías I. Liaudat, Matthijs Mars, Matthew A. Price, Marcelo Pereyra, Marta M. Betcke, Jason D. McEwen
2022 — —
Data-driven modelling of ground-based and space-based telescope's point spread functions. (Modélisation guidée par les données des fonctions d'étalement du point des télescopes terrestres et spatiaux).
Tobías I. Liaudat
2022 J jnl
Rethinking data-driven point spread function modeling with a differentiable optical model.
CoRR
Tobías I. Liaudat, Jean-Luc Starck, Martin Kilbinger, Pierre-Antoine Frugier
2021 J jnl
Rethinking the modeling of the instrumental response of telescopes with a differentiable optical model.
CoRR
Tobías I. Liaudat, Jean-Luc Starck, Martin Kilbinger, Pierre-Antoine Frugier
2020 J jnl
Faster and better sparse blind source separation through mini-batch optimization.
Digit. Signal Process.
Christophe Kervazo, Tobías I. Liaudat, Jérôme Bobin