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Found 15 papers, 13 papers with code
Mixed Noise and Posterior Estimation with Conditional DeepGEM
Motivated by indirect measurements and applications from nanometrology with a mixed noise model, we develop a novel algorithm for jointly estimating the posterior and the noise parameters in Bayesian inverse problems.
Fast Kernel Summation in High Dimensions via Slicing and Fourier Transforms
Kernel-based methods are heavily used in machine learning.
Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction
The solution of inverse problems is of fundamental interest in medical and astronomical imaging, geophysics as well as engineering and life sciences.
Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel
We propose conditional flows of the maximum mean discrepancy (MMD) with the negative distance kernel for posterior sampling and conditional generative modeling.
Generative Sliced MMD Flows with Riesz Kernels
We prove that the MMD of Riesz kernels, which is also known as energy distance, coincides with the MMD of their sliced version.
Manifold Learning by Mixture Models of VAEs for Inverse Problems
Representing a manifold of very high-dimensional data with generative models has been shown to be computationally efficient in practice.
Neural Wasserstein Gradient Flows for Maximum Mean Discrepancies with Riesz Kernels
Wasserstein gradient flows of maximum mean discrepancy (MMD) functionals with non-smooth Riesz kernels show a rich structure as singular measures can become absolutely continuous ones and conversely.
Proximal Residual Flows for Bayesian Inverse Problems
Normalizing flows are a powerful tool for generative modelling, density estimation and posterior reconstruction in Bayesian inverse problems.
PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization
Learning neural networks using only few available information is an important ongoing research topic with tremendous potential for applications.
WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolution
Exploiting image patches instead of whole images have proved to be a powerful approach to tackle various problems in image processing.
Generalized Normalizing Flows via Markov Chains
Normalizing flows, diffusion normalizing flows and variational autoencoders are powerful generative models.
Wasserstein Patch Prior for Image Superresolution
Then, the proposed regularizer penalizes the $W_2$-distance of the patch distribution of the reconstruction to the patch distribution of some reference image at different scales.
Stochastic Normalizing Flows for Inverse Problems: a Markov Chains Viewpoint
To overcome topological constraints and improve the expressiveness of normalizing flow architectures, Wu, K\"ohler and No\'e introduced stochastic normalizing flows which combine deterministic, learnable flow transformations with stochastic sampling methods.
Convolutional Proximal Neural Networks and Plug-and-Play Algorithms
In this paper, we introduce convolutional proximal neural networks (cPNNs), which are by construction averaged operators.
PCA Reduced Gaussian Mixture Models with Applications in Superresolution
To learn the (low dimensional) parameters of the mixture model we propose an EM algorithm whose M-step requires the solution of constrained optimization problems.
