Cover Image

Convergence and Recovery Guarantees of Unsupervised Neural Networks for Inverse Problems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Buskulic, Nathan, Fadili, Jalal, Quéau, Yvain
Format:	Preprint
Published:	2023
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Recovery Guarantees of Unsupervised Neural Networks for Inverse Problems trained with Gradient Descent
by: Buskulic, Nathan, et al.
Published: (2024)

Implicit Regularization of the Deep Inverse Prior Trained with Inertia
by: Buskulic, Nathan, et al.
Published: (2025)

Learning-to-Optimize with PAC-Bayesian Guarantees: Theoretical Considerations and Practical Implementation
by: Sucker, Michael, et al.
Published: (2024)

A Comparative Study of MAP and LMMSE Estimators for Blind Inverse Problems
by: Buskulic, Nathan, et al.
Published: (2026)

On the Sample Complexity of Learning for Blind Inverse Problems
by: Buskulic, Nathan, et al.
Published: (2025)

Solving Inverse Problems with Deep Linear Neural Networks: Global Convergence Guarantees for Gradient Descent with Weight Decay
by: Laus, Hannah, et al.
Published: (2025)

Towards Uncertainty Quantification in Generative Model Learning
by: Morales, Giorgio, et al.
Published: (2025)

A Recovery Guarantee for Sparse Neural Networks
by: Fridovich-Keil, Sara, et al.
Published: (2025)

Local Linear Recovery Guarantee of Deep Neural Networks at Overparameterization
by: Zhang, Yaoyu, et al.
Published: (2024)

On Model Compression for Neural Networks: Framework, Algorithm, and Convergence Guarantee
by: Li, Chenyang, et al.
Published: (2023)

Gradient Flow Convergence Guarantee for General Neural Network Architectures
by: Jakhmola, Yash
Published: (2025)

The Unreasonable Effectiveness of Solving Inverse Problems with Neural Networks
by: Holl, Philipp, et al.
Published: (2024)

Variational Graph Neural Networks for Uncertainty Quantification in Inverse Problems
by: Gonzalez, David, et al.
Published: (2026)

Unsupervised Attributed Dynamic Network Embedding with Stability Guarantees
by: Ceccherini, Emma, et al.
Published: (2025)

Learning Neural PDE Solvers with Convergence Guarantees
by: Hsieh, Jun-Ting, et al.
Published: (2019)

Neutrino Oscillation Parameter Estimation Using Structured Hierarchical Transformers
by: Morales, Giorgio, et al.
Published: (2026)

A plug-and-play approach with fast uncertainty quantification for weak lensing mass mapping
by: Leterme, Hubert, et al.
Published: (2026)

Improved Physics-Driven Neural Network to Solve Inverse Scattering Problems
by: Du, Yutong, et al.
Published: (2025)

Coefficient-to-Basis Network: A Fine-Tunable Operator Learning Framework for Inverse Problems with Adaptive Discretizations and Theoretical Guarantees
by: Zhang, Zecheng, et al.
Published: (2025)

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems
by: Goyal, Girnar, et al.
Published: (2025)

HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems
by: Chen, Qiyue, et al.
Published: (2025)

Photometric Stereo using Gaussian Splatting and inverse rendering
by: Ducastel, Matéo, et al.
Published: (2025)

Unit Commitment Predictor With a Performance Guarantee: A Support Vector Machine Classifier
by: Pourahmadi, Farzaneh, et al.
Published: (2023)

Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems
by: Altekrüger, Fabian, et al.
Published: (2023)

Neural Optimal Design of Experiment for Inverse Problems
by: Darges, John E., et al.
Published: (2025)

Learning Generalizable Neural Operators for Inverse Problems
by: Thorpe, Adam J., et al.
Published: (2025)

Theoretical Convergence Guarantees for Variational Autoencoders
by: Surendran, Sobihan, et al.
Published: (2024)

Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: Global Convergence Guarantees and Feature Learning
by: Caron, Francois, et al.
Published: (2023)

A Spectral Framework for Graph Neural Operators: Convergence Guarantees and Tradeoffs
by: Holden, Roxanne, et al.
Published: (2025)

Symmetry Guarantees Statistic Recovery in Variational Inference
by: Marks, Daniel, et al.
Published: (2026)

A Model-Guided Neural Network Method for the Inverse Scattering Problem
by: Tsang, Olivia, et al.
Published: (2025)

Recommendations with Sparse Comparison Data: Provably Fast Convergence for Nonconvex Matrix Factorization
by: Sankagiri, Suryanarayana, et al.
Published: (2025)

Unsupervised Model Tree Heritage Recovery
by: Horwitz, Eliahu, et al.
Published: (2024)

Unsupervised Prompting for Graph Neural Networks
by: Baghershahi, Peyman, et al.
Published: (2025)

A Deep Neural Network Framework for Solving Forward and Inverse Problems in Delay Differential Equations
by: Wang, Housen, et al.
Published: (2024)

Purification Of Contaminated Convolutional Neural Networks Via Robust Recovery: An Approach with Theoretical Guarantee in One-Hidden-Layer Case
by: Lu, Hanxiao, et al.
Published: (2024)

Contrast-Source-Based Physics-Driven Neural Network for Inverse Scattering Problems
by: Du, Yutong, et al.
Published: (2026)

Solving the Wide-band Inverse Scattering Problem via Equivariant Neural Networks
by: Zhang, Borong, et al.
Published: (2022)

Active Seriation: Efficient Ordering Recovery with Statistical Guarantees
by: Cheshire, James, et al.
Published: (2026)

Unsupervised Imaging Inverse Problems with Diffusion Distribution Matching
by: Meanti, Giacomo, et al.
Published: (2025)