Generalized Normalizing Flows via Markov Chains

24 November 2021

Papers citing "Generalized Normalizing Flows via Markov Chains"

20 / 20 papers shown

Title
From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster training Julius Berner Lorenz Richter Marcin Sendera Jarrid Rector-Brooks Nikolay Malkin OffRL 56 3 0 10 Jan 2025
S-Diff: An Anisotropic Diffusion Model for Collaborative Filtering in Spectral Domain Rui Xia Yanhua Cheng Yongxiang Tang Xiaocheng Liu Xialong Liu Lisong Wang Peng Jiang DiffM 28 0 0 03 Jan 2025
Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching Jannis Chemseddine Paul Hagemann Gabriele Steidl Christian Wald 38 9 0 27 Mar 2024
Mixed Noise and Posterior Estimation with Conditional DeepGEM Paul Hagemann J. Hertrich Maren Casfor Sebastian Heidenreich Gabriele Steidl 14 0 0 05 Feb 2024
Manifold GCN: Diffusion-based Convolutional Neural Network for Manifold-valued Graphs M. Hanik Gabriele Steidl C. V. Tycowicz GNN MedIm 19 3 0 25 Jan 2024
Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction Moritz Piening Fabian Altekrüger J. Hertrich Paul Hagemann Andrea Walther Gabriele Steidl 19 5 0 27 Dec 2023
Y-Diagonal Couplings: Approximating Posteriors with Conditional Wasserstein Distances Jannis Chemseddine Paul Hagemann Christian Wald 27 3 0 20 Oct 2023
Generalised Diffusion Probabilistic Scale-Spaces Pascal Peter DiffM 22 0 0 15 Sep 2023
Variations and Relaxations of Normalizing Flows Keegan Kelly Lorena Piedras Sukrit Rao David Samuel Roth BDL 25 0 0 08 Sep 2023
Generative Sliced MMD Flows with Riesz Kernels J. Hertrich Christian Wald Fabian Altekrüger Paul Hagemann 23 23 0 19 May 2023
Refining Amortized Posterior Approximations using Gradient-Based Summary Statistics Rafael Orozco Ali Siahkoohi M. Louboutin Felix J. Herrmann 10 3 0 15 May 2023
NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problems Ziruo Cai Junqi Tang Subhadip Mukherjee Jinglai Li Carola Bibiane Schönlieb Xiaoqun Zhang AI4CE 23 3 0 17 Apr 2023
Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems Fabian Altekrüger Paul Hagemann Gabriele Steidl TPM 16 9 0 28 Mar 2023
Manifold Learning by Mixture Models of VAEs for Inverse Problems Giovanni S. Alberti J. Hertrich Matteo Santacesaria Silvia Sciutto DRL 13 6 0 27 Mar 2023
Generalised Scale-Space Properties for Probabilistic Diffusion Models Pascal Peter DiffM 21 1 0 14 Mar 2023
Multilevel Diffusion: Infinite Dimensional Score-Based Diffusion Models for Image Generation Paul Hagemann Sophie Mildenberger Lars Ruthotto Gabriele Steidl Ni Yang DiffM 53 20 0 08 Mar 2023
Neural Wasserstein Gradient Flows for Maximum Mean Discrepancies with Riesz Kernels Fabian Altekrüger J. Hertrich Gabriele Steidl 22 13 0 27 Jan 2023
Proximal Residual Flows for Bayesian Inverse Problems J. Hertrich BDL TPM 10 4 0 30 Nov 2022
WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolution Fabian Altekrüger J. Hertrich 19 15 0 20 Jan 2022
Invertible Neural Networks versus MCMC for Posterior Reconstruction in Grazing Incidence X-Ray Fluorescence A. Andrle N. Farchmin Paul Hagemann Sebastian Heidenreich V. Soltwisch Gabriele Steidl 58 16 0 05 Feb 2021