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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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03/28/2023
Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems
Conditional generative models became a very powerful tool to sample from...
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03/08/2023
Multilevel Diffusion: Infinite Dimensional Score-Based Diffusion Models for Image Generation
Score-based diffusion models (SBDM) have recently emerged as state-of-th...
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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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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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09/23/2021
Stochastic Normalizing Flows for Inverse Problems: a Markov Chains Viewpoint
To overcome topological constraints and improve the expressiveness of no...
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02/05/2021
Invertible Neural Networks versus MCMC for Posterior Reconstruction in Grazing Incidence X-Ray Fluorescence
Grazing incidence X-ray fluorescence is a non-destructive technique for ...
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09/07/2020
