Poster "inverse problems" Papers
35 papers found
Conference
Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models Trained on Corrupted Data
Asad Aali, Giannis Daras, Brett Levac et al.
Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation
Da Long, Zhitong Xu, Guang Yang et al.
Boundary constrained Gaussian processes for robust physics-informed machine learning of linear partial differential equations
David Dalton, Alan Lazarus, Hao Gao et al.
Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling
Michal Balcerak, Tamaz Amiranashvili, Antonio Terpin et al.
LATINO-PRO: LAtent consisTency INverse sOlver with PRompt Optimization
Alessio Spagnoletti, Jean Prost, Andres Almansa et al.
Manifold Learning by Mixture Models of VAEs for Inverse Problems
Giovanni S. Alberti, Johannes Hertrich, Matteo Santacesaria et al.
MAP Estimation with Denoisers: Convergence Rates and Guarantees
Scott Pesme, Giacomo Meanti, Michael Arbel et al.
PIED: Physics-Informed Experimental Design for Inverse Problems
Apivich Hemachandra, Gregory Kang Ruey Lau, See-Kiong Ng et al.
PRDP: Progressively Refined Differentiable Physics
Kanishk Bhatia, Felix Koehler, Nils Thuerey
Repulsive Latent Score Distillation for Solving Inverse Problems
Nicolas Zilberstein, Morteza Mardani, Santiago Segarra
Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior
Tongda Xu, Xiyan Cai, Xinjie Zhang et al.
Rethinking Gradient Step Denoiser: Towards Truly Pseudo-Contractive Operator
Shuchang Zhang, Yaoyun Zeng, Kangkang Deng et al.
Self-diffusion for Solving Inverse Problems
Guanxiong Luo, Shoujin Huang
Semialgebraic Neural Networks: From roots to representations
S David Mis, Matti Lassas, Maarten V de Hoop
Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations
Abdolmehdi Behroozi, Chaopeng Shen, Daniel Kifer
Solving and Learning Partial Differential Equations with Variational Q-Exponential Processes
Guangting Yu, Shiwei Lan
Solving Inverse Problems with FLAIR
Julius Erbach, Dominik Narnhofer, Andreas Dombos et al.
Solving Inverse Problems with Model Mismatch using Untrained Neural Networks within Model-based Architectures
Peimeng Guan, Naveed Iqbal, Mark Davenport et al.
Split Gibbs Discrete Diffusion Posterior Sampling
Wenda Chu, Zihui Wu, Yifan Chen et al.
System-Embedded Diffusion Bridge Models
Bartlomiej Sobieski, Matthew Tivnan, Yuang Wang et al.
Time-Embedded Algorithm Unrolling for Computational MRI
Junno Yun, Yasar Utku Alcalar, Mehmet Akcakaya
Deep Diffusion Image Prior for Efficient OOD Adaptation in 3D Inverse Problems
Hyungjin Chung, Jong Chul Ye
D-Flow: Differentiating through Flows for Controlled Generation
Heli Ben-Hamu, Omri Puny, Itai Gat et al.
Diffusion Posterior Sampling is Computationally Intractable
Shivam Gupta, Ajil Jalal, Aditya Parulekar et al.
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse Problems
Sojin Lee, Dogyun Park, Inho Kong et al.
Global Optimality for Non-linear Constrained Restoration Problems via Invexity
Samuel Pinilla, Jeyan Thiyagalingam
Improving Diffusion Models for Inverse Problems Using Optimal Posterior Covariance
Xinyu Peng, Ziyang Zheng, Wenrui Dai et al.
Learning Pseudo-Contractive Denoisers for Inverse Problems
Deliang Wei, Peng Chen, Fang Li
Plug-and-Play image restoration with Stochastic deNOising REgularization
Marien Renaud, Jean Prost, Arthur Leclaire et al.
Plug-and-Play Learned Proximal Trajectory for 3D Sparse-View X-Ray Computed Tomography
Romain Vo, Julie Escoda, Caroline Vienne et al.
Prompt-tuning Latent Diffusion Models for Inverse Problems
Hyungjin Chung, Jong Chul YE, Peyman Milanfar et al.
Task-Driven Uncertainty Quantification in Inverse Problems via Conformal Prediction
Jeffrey Wen, Rizwan Ahmad, Phillip Schniter
The Emergence of Reproducibility and Consistency in Diffusion Models
Huijie Zhang, Jinfan Zhou, Yifu Lu et al.
Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation
Zakhar Shumaylov, Jeremy Budd, Subhadip Mukherjee et al.
Zero-Shot Adaptation for Approximate Posterior Sampling of Diffusion Models in Inverse Problems
Yasar Utku Alcalar, Mehmet Akcakaya