Image de couverture de livre

Why the noise model matters: A performance gap in learned regularization

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Banert, Sebastian, Brauer, Christoph, Lorenz, Dirk, Tondji, Lionel
Format:	Preprint
Publié:	2025
Sujets:	Numerical Analysis Machine Learning 65J20, 68T05
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Acceleration and restart for the randomized Bregman-Kaczmarz method
par: Tondji, Lionel, et autres
Publié: (2023)

An accelerated randomized Bregman-Kaczmarz method for strongly convex linearly constraint optimization
par: Tondji, Lionel, et autres
Publié: (2025)

A Theory of Non-Acyclic Generative Flow Networks
par: Brunswic, Leo Maxime, et autres
Publié: (2023)

Adaptive Bregman-Kaczmarz: An Approach to Solve Linear Inverse Problems with Independent Noise Exactly
par: Tondji, Lionel, et autres
Publié: (2023)

A resource-efficient model for deep kernel learning
par: D'Amore, Luisa
Publié: (2024)

Physics-informed features in supervised machine learning
par: Lampani, Margherita, et autres
Publié: (2025)

Reconstruction of Boundary Data in the Helmholtz Equation Using Particle Swarm Optimization
par: Daoudi, Jamal, et autres
Publié: (2025)

Sequential Least-Squares Estimators with Fast Randomized Sketching for Linear Statistical Models
par: Chen, Guan-Yu, et autres
Publié: (2025)

Interpolation and inverse problems in spectral Barron spaces
par: Lu, Shuai, et autres
Publié: (2025)

Sampling Distributions as Regularization in Learned Inverse Problems
par: Babyale, Sandra R., et autres
Publié: (2026)

Learned Regularization for Inverse Problems: Insights from a Spectral Model
par: Burger, Martin, et autres
Publié: (2023)

Optimization-Free Diffusion Model -- A Perturbation Theory Approach
par: Khoo, Yuehaw, et autres
Publié: (2025)

Physics-informed low-rank neural operators with application to parametric elliptic PDEs
par: Schaffer, Sebastian, et autres
Publié: (2025)

On regularized polynomial functional regression
par: Holzleitner, Markus, et autres
Publié: (2023)

Regularized Random Fourier Features and Finite Element Reconstruction for Operator Learning in Sobolev Space
par: Yu, Xinyue, et autres
Publié: (2025)

Deep neural network yields regularization for ill-posed inverse problems
par: Zhu, Qiao, et autres
Publié: (2026)

On recovering the Radon-Nikodym derivative under the big data assumption
par: Myleiko, Hanna, et autres
Publié: (2025)

Operator Learning Using Random Features: A Tool for Scientific Computing
par: Nelsen, Nicholas H., et autres
Publié: (2024)

High-performance matrix-free unfitted finite element operator evaluation
par: Bergbauer, Maximilian, et autres
Publié: (2024)

Stochastic Trace Optimization of Parameter Dependent Matrices Based on Statistical Learning Theory
par: Saibaba, Arvind K., et autres
Publié: (2025)

Equation-Free Coarse Control of Distributed Parameter Systems via Local Neural Operators
par: Fabiani, Gianluca, et autres
Publié: (2025)

Leveraging Operator Learning to Accelerate Convergence of the Preconditioned Conjugate Gradient Method
par: Kopaničáková, Alena, et autres
Publié: (2025)

Consistency of Learned Sparse Grid Quadrature Rules using NeuralODEs
par: Gottschalk, Hanno, et autres
Publié: (2025)

On inertial Levenberg-Marquardt type methods for solving nonlinear ill-posed operator equations
par: Leitão, Antonio, et autres
Publié: (2024)

A short tour of operator learning theory: Convergence rates, statistical limits, and open questions
par: Brugiapaglia, Simone, et autres
Publié: (2026)

Approximation of the Basset force in the Maxey-Riley-Gatignol equations via universal differential equations
par: Sommer, Finn, et autres
Publié: (2026)

A hybrid interpolation ACA accelerated method for parabolic boundary integral operators
par: Ambikasaran, Sivaram, et autres
Publié: (2024)

XTrace: Making the most of every sample in stochastic trace estimation
par: Epperly, Ethan N., et autres
Publié: (2023)

The Delaunay Density Diagnostic
par: Gillette, Andrew, et autres
Publié: (2022)

Two Variations on the XTrace Algorithm
par: Hallman, Eric
Publié: (2025)

RandONet: Shallow-Networks with Random Projections for learning linear and nonlinear operators
par: Fabiani, Gianluca, et autres
Publié: (2024)

Convergence for adaptive resampling of random Fourier features
par: Huang, Xin, et autres
Publié: (2025)

A mesh-free method for interface problems using the deep learning approach
par: Wang, Zhongjian, et autres
Publié: (2019)

How Can Deep Neural Networks Fail Even With Global Optima?
par: Guan, Qingguang
Publié: (2024)

Neural-HSS: Hierarchical Semi-Separable Neural PDE Solver
par: Sittoni, Pietro, et autres
Publié: (2026)

Randomly pivoted Cholesky: Practical approximation of a kernel matrix with few entry evaluations
par: Chen, Yifan, et autres
Publié: (2022)

Accurately recover global quasiperiodic systems by finite points
par: Jiang, Kai, et autres
Publié: (2023)

Recurrent Neural Operators: Stable Long-Term PDE Prediction
par: Ye, Zaijun, et autres
Publié: (2025)

Deep Unfolding Network for Nonlinear Multi-Frequency Electrical Impedance Tomography
par: Alberti, Giovanni S., et autres
Publié: (2025)

Randomized batch-sampling Kaczmarz methods for solving linear systems
par: Xie, Dong-Yue, et autres
Publié: (2025)