Cover Image

Variational Garrote for Sparse Inverse Problems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lee, Kanghun, Soh, Hyungjoon, Jo, Junghyo
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Variational Garrote for Statistical Physics-based Sparse and Robust Variable Selection
by: Soh, Hyungjoon, et al.
Published: (2025)

Stochastic Clock Attention for Aligning Continuous and Ordered Sequences
by: Soh, Hyungjoon, et al.
Published: (2025)

Resolution Chromatography of Diffusion Models
by: Hwang, Juno, et al.
Published: (2023)

Geometric Remove-and-Retrain (GOAR): Coordinate-Invariant eXplainable AI Assessment
by: Park, Yong-Hyun, et al.
Published: (2024)

Measurement-Consistent Langevin Corrector for Stabilizing Latent Diffusion Inverse Problem Solvers
by: Hyoseok, Lee, et al.
Published: (2026)

Multiplicative learning from observation-prediction ratios
by: Kim, Han, et al.
Published: (2025)

Simple yet Effective Semi-supervised Knowledge Distillation from Vision-Language Models via Dual-Head Optimization
by: Kang, Seongjae, et al.
Published: (2025)

Consistency Regularised Gradient Flows for Inverse Problems
by: Spagnoletti, Alessio, et al.
Published: (2026)

Robust Network Learning via Inverse Scale Variational Sparsification
by: Zhou, Zhiling, et al.
Published: (2024)

Measurement Geometry and Design for Trustworthy Generative Inverse Problems
by: Jin, Pengfei, et al.
Published: (2026)

Flower: A Flow-Matching Solver for Inverse Problems
by: Pourya, Mehrsa, et al.
Published: (2025)

Fast Samplers for Inverse Problems in Iterative Refinement Models
by: Pandey, Kushagra, et al.
Published: (2024)

An Over Complete Deep Learning Method for Inverse Problems
by: Eliasof, Moshe, et al.
Published: (2024)

Repulsive Latent Score Distillation for Solving Inverse Problems
by: Zilberstein, Nicolas, et al.
Published: (2024)

InverseCrafter: Efficient Video ReCapture as a Latent Domain Inverse Problem
by: Hong, Yeobin, et al.
Published: (2025)

P-Flow: Proxy-gradient Flows for Linear Inverse Problems
by: Jiang, Zehua, et al.
Published: (2026)

On Hallucinations in Inverse Problems: Fundamental Limits and Provable Assessment Methods
by: Iagaru, David, et al.
Published: (2026)

What's in a Prior? Learned Proximal Networks for Inverse Problems
by: Fang, Zhenghan, et al.
Published: (2023)

Inverse Problems with Diffusion Models: A MAP Estimation Perspective
by: Gutha, Sai Bharath Chandra, et al.
Published: (2024)

Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems
by: Chung, Hyungjin, et al.
Published: (2023)

Device Image-IV Mapping using Variational Autoencoder for Inverse Design and Forward Prediction
by: Lu, Thomas, et al.
Published: (2023)

Differentiable Gaussianization Layers for Inverse Problems Regularized by Deep Generative Models
by: Li, Dongzhuo
Published: (2021)

Improving Diffusion Models for Inverse Problems Using Optimal Posterior Covariance
by: Peng, Xinyu, et al.
Published: (2024)

DMPlug: A Plug-in Method for Solving Inverse Problems with Diffusion Models
by: Wang, Hengkang, et al.
Published: (2024)

Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory Matching
by: Zhang, Yasi, et al.
Published: (2024)

Diffusion Models for Solving Inverse Problems via Posterior Sampling with Piecewise Guidance
by: Mohseni-Sehdeh, Saeed, et al.
Published: (2025)

Improving Decoupled Posterior Sampling for Inverse Problems using Data Consistency Constraint
by: Qi, Zhi, et al.
Published: (2024)

Weak Diffusion Priors Can Still Achieve Strong Inverse-Problem Performance
by: Jia, Jing, et al.
Published: (2026)

Curvature-Regularized Variational Autoencoder for 3D Scene Reconstruction from Sparse Depth
by: Yousefi, Maryam, et al.
Published: (2025)

Deep Diffusion Image Prior for Efficient OOD Adaptation in 3D Inverse Problems
by: Chung, Hyungjin, et al.
Published: (2024)

OTLRM: Orthogonal Learning-based Low-Rank Metric for Multi-Dimensional Inverse Problems
by: Wang, Xiangming, et al.
Published: (2024)

SAIP: A Plug-and-Play Scale-adaptive Module in Diffusion-based Inverse Problems
by: Wang, Lingyu, et al.
Published: (2025)

KLIP: localized distribution shift detection via KL-divergence with diffusion priors in Inverse Problems
by: Kheirandish, Alireza, et al.
Published: (2026)

Align & Invert: Solving Inverse Problems with Diffusion and Flow-based Models via Representation Alignment
by: Sfountouris, Loukas, et al.
Published: (2025)

Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models
by: Zheng, Yang, et al.
Published: (2025)

Contrastive Diffusion Guidance for Spatial Inverse Problems
by: Basu, Sattwik, et al.
Published: (2025)

Bayesian Conditioned Diffusion Models for Inverse Problems
by: Güngör, Alper, et al.
Published: (2024)

A Survey on Diffusion Models for Inverse Problems
by: Daras, Giannis, et al.
Published: (2024)

SILO: Solving Inverse Problems with Latent Operators
by: Raphaeli, Ron, et al.
Published: (2025)

Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overview
by: Auras, Alexander, et al.
Published: (2024)