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05/19/2023
Generative Sliced MMD Flows with Riesz Kernels
Maximum mean discrepancy (MMD) flows suffer from high computational cost...
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03/27/2023
Manifold Learning by Mixture Models of VAEs for Inverse Problems
Representing a manifold of very high-dimensional data with generative mo...
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01/27/2023
Neural Wasserstein Gradient Flows for Maximum Mean Discrepancies with Riesz Kernels
Wasserstein gradient flows of maximum mean discrepancy (MMD) functionals...
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01/11/2023
Wasserstein Gradient Flows of the Discrepancy with Distance Kernel on the Line
This paper provides results on Wasserstein gradient flows between measur...
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11/30/2022
Proximal Residual Flows for Bayesian Inverse Problems
Normalizing flows are a powerful tool for generative modelling, density ...
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11/02/2022
Wasserstein Steepest Descent Flows of Discrepancies with Riesz Kernels
The aim of this paper is twofold. Based on the geometric Wasserstein tan...
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05/24/2022
PatchNR: Learning from Small Data by Patch Normalizing Flow Regularization
Learning neural networks using only a small amount of data is an importa...
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01/20/2022
WPPNets: Unsupervised CNN Training with Wasserstein Patch Priors for Image Superresolution
We introduce WPPNets, which are CNNs trained by a new unsupervised loss ...
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11/24/2021
A Unified Approach to Variational Autoencoders and Stochastic Normalizing Flows via Markov Chains
Normalizing flows, diffusion normalizing flows and variational autoencod...
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09/27/2021
Wasserstein Patch Prior for Image Superresolution
In this paper, we introduce a Wasserstein patch prior for superresolutio...
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09/23/2021
Stochastic Normalizing Flows for Inverse Problems: a Markov Chains Viewpoint
To overcome topological constraints and improve the expressiveness of no...
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05/31/2021
Sparse ANOVA Inspired Mixture Models
Based on the analysis of variance (ANOVA) decomposition of functions whi...
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11/04/2020
Convolutional Proximal Neural Networks and Plug-and-Play Algorithms
In this paper, we introduce convolutional proximal neural networks (cPNN...
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09/16/2020
PCA Reduced Gaussian Mixture Models with Applications in Superresolution
Despite the rapid development of computational hardware, the treatment o...
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05/05/2020
Inertial Stochastic PALM and its Application for Learning Student-t Mixture Models
Inertial algorithms for minimizing nonsmooth and nonconvex functions as ...
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12/19/2019
Parseval Proximal Neural Networks
The aim of this paper is twofold. First, we show that a certain concaten...
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10/25/2019
Infinity-Laplacians on Scalar- and Vector-Valued Functions and Optimal Lipschitz Extensions on Graphs
Extending functions from boundary values plays an important role in vari...
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10/15/2019
