Jalal Fadili

Centre National de la Recherche Scientifique — France

Engineering · Physical Sciences

37h-index6.6kcitations357works0.72yr avg
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Research Topics

Sparse and Compressive Sensing Techniques(126), Image and Signal Denoising Methods(95), Numerical methods in inverse problems(57), Medical Image Segmentation Techniques(53), Statistical Methods and Inference(43)

Publications357 total

Towards Uncertainty Quantification in Generative Model Learning
arXiv (Cornell University)·2025Open Access
A New Perspective on Precision and Recall for Generative Models
arXiv (Cornell University)·2025Open Access
The Iterates of Nesterov's Accelerated Algorithm Converge in The Critical Regimes
arXiv (Cornell University)·2025Open Access
Satellite Detection and Characterization Using Alternate Transformer Architectures for Spectro–Spatial RF Signal Analysis
IEEE Transactions on Aerospace and Electronic Systems·2025
An SDE perspective on stochastic inertial gradient dynamics with time-dependent viscosity and Hessian-driven damping
Optimization·2025Open Access
Solution Uniqueness of Convex Optimization Problems via the Radial Cone
Journal of Optimization Theory and Applications·2025· 3 cited
Simulation-Augmented Physics-Aware Neural Networks for Nonlinear Inverse Problems
SPIRE - Sciences Po Institutional REpository·2025Open Access
Stochastic monotone inclusion with closed loop distributions
Evolution equations and control theory·2025Open Access
Quasi-Newton Methods for Monotone Inclusions: Efficient Resolvent Calculus and Primal-Dual Algorithms
SIAM Journal on Imaging Sciences·2025· 1 cited
Distribution-free uncertainty quantification for inverse problems: Application to weak lensing mass mapping
Astronomy and Astrophysics·2025Open Access
An Stochastic Differential Equation Perspective on Stochastic Convex Optimization
Mathematics of Operations Research·2024· 3 cited
Fast Krasnoselskii-Mann Method with Overrelaxation and Preconditioners
arXiv (Cornell University)·2024Open Access
Comparative analysis of accelerated gradient algorithms for convex optimization: high and super resolution ODE approach
Optimization·2024· 1 citedOpen Access
Distribution-free uncertainty quantification for inverse problems: application to weak lensing mass mapping
arXiv (Cornell University)·2024Open Access
Limits of non-local approximations to the Eikonal equation on manifolds
arXiv (Cornell University)·2024Open Access
Recovery Guarantees of Unsupervised Neural Networks for Inverse Problems Trained with Gradient Descent
2024Open Access
Low Complexity Regularized Phase Retrieval
arXiv (Cornell University)·2024Open Access
Inertial Methods with Viscous and Hessian driven Damping for Non-Convex Optimization
arXiv (Cornell University)·2024Open Access
An SDE Perspective on Stochastic Inertial Gradient Dynamics with Time-Dependent Viscosity and Geometric Damping
arXiv (Cornell University)·2024· 1 citedOpen Access
Convergence and Recovery Guarantees of Unsupervised Neural Networks for Inverse Problems
Journal of Mathematical Imaging and Vision·2024· 4 citedOpen Access
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Frequent Co-authors

Jean‐Luc Starck(21), Gabriel Peyré(13), Edward T. Bullmore(4), J. Bobin(4), Y. Moudden(4), Jingwei Liang(4), John Suckling(3), Fionn Murtagh(3), Larbi Boubchir(3), David L. Donoho(2), L. Sendur(2), Su Ruan(2), D. Bloyet(2), Fionn Murtagh(2), Charles Dossal(2), Samuel Vaiter(2), Chris Long(1), Gemma A. Calvert(1), Fernando Zelaya(1), T. Adrian Carpenter(1)