Learning a Gaussian Mixture for Sparsity Regularization in Inverse Problems
Fuente:
arXiv
Saved in:
| Main Authors: | Alberti, Giovanni S., Ratti, Luca, Santacesaria, Matteo, Sciutto, Silvia |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Manifold Learning by Mixture Models of VAEs for Inverse Problems
by: Alberti, Giovanni S., et al.
Published: (2023)
by: Alberti, Giovanni S., et al.
Published: (2023)
Continuous Generative Neural Networks: A Wavelet-Based Architecture in Function Spaces
by: Alberti, Giovanni S., et al.
Published: (2022)
by: Alberti, Giovanni S., et al.
Published: (2022)
Deep Unfolding Network for Nonlinear Multi-Frequency Electrical Impedance Tomography
by: Alberti, Giovanni S., et al.
Published: (2025)
by: Alberti, Giovanni S., et al.
Published: (2025)
Learning sparsity-promoting regularizers for linear inverse problems
by: Alberti, Giovanni S., et al.
Published: (2024)
by: Alberti, Giovanni S., et al.
Published: (2024)
MAD: Manifold Attracted Diffusion
by: Elbrächter, Dennis, et al.
Published: (2025)
by: Elbrächter, Dennis, et al.
Published: (2025)
Diffusion Graph Posterior Sampling for Nonlinear Inverse Problems with Application to Electrical Impedance Tomography
by: Alberti, Giovanni S., et al.
Published: (2026)
by: Alberti, Giovanni S., et al.
Published: (2026)
On the Sample Complexity of Learning for Blind Inverse Problems
by: Buskulic, Nathan, et al.
Published: (2025)
by: Buskulic, Nathan, et al.
Published: (2025)
Learning Regularization for Graph Inverse Problems
by: Eliasof, Moshe, et al.
Published: (2024)
by: Eliasof, Moshe, et al.
Published: (2024)
Inverse problems for quasi-linear elliptic systems modeling electrolysers
by: Alberti, Giovanni S., et al.
Published: (2026)
by: Alberti, Giovanni S., et al.
Published: (2026)
Stable Derivative Free Gaussian Mixture Variational Inference for Bayesian Inverse Problems
by: Che, Baojun, et al.
Published: (2025)
by: Che, Baojun, et al.
Published: (2025)
Graph-Regularized Learning of Gaussian Mixture Models
by: Abdurakhmanova, Shamsiiat, et al.
Published: (2025)
by: Abdurakhmanova, Shamsiiat, et al.
Published: (2025)
Differentiable Gaussianization Layers for Inverse Problems Regularized by Deep Generative Models
by: Li, Dongzhuo
Published: (2021)
by: Li, Dongzhuo
Published: (2021)
Sparsity and Superposition in Mixture of Experts
by: Chaudhari, Marmik, et al.
Published: (2025)
by: Chaudhari, Marmik, et al.
Published: (2025)
Graph Neural Regularizers for PDE Inverse Problems
by: Lauga, William, et al.
Published: (2025)
by: Lauga, William, et al.
Published: (2025)
Learned Finite Element-based Regularization of the Inverse Problem in Electrocardiographic Imaging
by: Haas, Manuel, et al.
Published: (2026)
by: Haas, Manuel, et al.
Published: (2026)
Learning Regularization Functionals for Inverse Problems: A Comparative Study
by: Hertrich, Johannes, et al.
Published: (2025)
by: Hertrich, Johannes, et al.
Published: (2025)
Stability of Data-Dependent Ridge-Regularization for Inverse Problems
by: Neumayer, Sebastian, et al.
Published: (2024)
by: Neumayer, Sebastian, et al.
Published: (2024)
EquiReg: Equivariance Regularized Diffusion for Inverse Problems
by: Tolooshams, Bahareh, et al.
Published: (2025)
by: Tolooshams, Bahareh, et al.
Published: (2025)
Reinforcement Learning With Sparse-Executing Actions via Sparsity Regularization
by: Pang, Jing-Cheng, et al.
Published: (2021)
by: Pang, Jing-Cheng, et al.
Published: (2021)
On the Inverse Flow Matching Problem in the One-Dimensional and Gaussian Cases
by: Korotin, Alexander, et al.
Published: (2025)
by: Korotin, Alexander, et al.
Published: (2025)
A Stability Benchmark of Generative Regularizers for Inverse Problems
by: Denker, Alexander, et al.
Published: (2026)
by: Denker, Alexander, et al.
Published: (2026)
Sparsity via Sparse Group $k$-max Regularization
by: Tao, Qinghua, et al.
Published: (2024)
by: Tao, Qinghua, et al.
Published: (2024)
Adaptive Regularization for Sparsity Control in Bregman-Based Optimizers
by: Aloradi, Ahmad, et al.
Published: (2026)
by: Aloradi, Ahmad, et al.
Published: (2026)
Latent Spectral Regularization for Continual Learning
by: Frascaroli, Emanuele, et al.
Published: (2023)
by: Frascaroli, Emanuele, et al.
Published: (2023)
Gaussian Process Regression for Inverse Problems in Linear PDEs
by: Li, Xin, et al.
Published: (2025)
by: Li, Xin, et al.
Published: (2025)
discretize_distributions: Efficient Quantization of Gaussian Mixtures with Guarantees in Wasserstein Distance
by: Adams, Steven, et al.
Published: (2025)
by: Adams, Steven, et al.
Published: (2025)
Regularized Schrödinger Bridge: Alleviating Distortion and Exposure Bias in Solving Inverse Problems
by: Yao, Qing, et al.
Published: (2025)
by: Yao, Qing, et al.
Published: (2025)
Generalization Guarantees for Multi-View Representation Learning and Application to Regularization via Gaussian Product Mixture Prior
by: Sefidgaran, Milad, et al.
Published: (2025)
by: Sefidgaran, Milad, et al.
Published: (2025)
Exploiting Activation Sparsity with Dense to Dynamic-k Mixture-of-Experts Conversion
by: Szatkowski, Filip, et al.
Published: (2023)
by: Szatkowski, Filip, et al.
Published: (2023)
ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs
by: Liu, Hongyi, et al.
Published: (2025)
by: Liu, Hongyi, et al.
Published: (2025)
LoRA Dropout as a Sparsity Regularizer for Overfitting Control
by: Lin, Yang, et al.
Published: (2024)
by: Lin, Yang, et al.
Published: (2024)
Exact Evaluation of the Accuracy of Diffusion Models for Inverse Problems with Gaussian Data Distributions
by: Pierret, Emile, et al.
Published: (2025)
by: Pierret, Emile, et al.
Published: (2025)
Learned Regularization for Inverse Problems: Insights from a Spectral Model
by: Burger, Martin, et al.
Published: (2023)
by: Burger, Martin, et al.
Published: (2023)
Learning Generalizable Neural Operators for Inverse Problems
by: Thorpe, Adam J., et al.
Published: (2025)
by: Thorpe, Adam J., et al.
Published: (2025)
Learning Difference-of-Convex Regularizers for Inverse Problems: A Flexible Framework with Theoretical Guarantees
by: Zhang, Yasi, et al.
Published: (2025)
by: Zhang, Yasi, et al.
Published: (2025)
High-dimensional Contextual Bandit Problem without Sparsity
by: Komiyama, Junpei, et al.
Published: (2023)
by: Komiyama, Junpei, et al.
Published: (2023)
Multi-Task Learning for Sparsity Pattern Heterogeneity: Statistical and Computational Perspectives
by: Behdin, Kayhan, et al.
Published: (2022)
by: Behdin, Kayhan, et al.
Published: (2022)
Localization of point scatterers via sparse optimization on measures
by: Alberti, Giovanni S., et al.
Published: (2024)
by: Alberti, Giovanni S., et al.
Published: (2024)
Invertible ResNets for Inverse Imaging Problems: Competitive Performance with Provable Regularization Properties
by: Arndt, Clemens, et al.
Published: (2024)
by: Arndt, Clemens, et al.
Published: (2024)
Sparse Mixture-of-Experts for Compositional Generalization: Empirical Evidence and Theoretical Foundations of Optimal Sparsity
by: Zhao, Jinze, et al.
Published: (2024)
by: Zhao, Jinze, et al.
Published: (2024)
Similar Items
-
Manifold Learning by Mixture Models of VAEs for Inverse Problems
by: Alberti, Giovanni S., et al.
Published: (2023) -
Continuous Generative Neural Networks: A Wavelet-Based Architecture in Function Spaces
by: Alberti, Giovanni S., et al.
Published: (2022) -
Deep Unfolding Network for Nonlinear Multi-Frequency Electrical Impedance Tomography
by: Alberti, Giovanni S., et al.
Published: (2025) -
Learning sparsity-promoting regularizers for linear inverse problems
by: Alberti, Giovanni S., et al.
Published: (2024) -
MAD: Manifold Attracted Diffusion
by: Elbrächter, Dennis, et al.
Published: (2025)