Theoretical guarantees for sampling and inference in generative models with latent diffusions

5 March 2019

Papers citing "Theoretical guarantees for sampling and inference in generative models with latent diffusions"

28 / 28 papers shown

Title
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching Aaron J. Havens Benjamin Kurt Miller Bing Yan Carles Domingo-Enrich Anuroop Sriram ... Brandon Amos Brian Karrer Xiang Fu Guan-Horng Liu Ricky T. Q. Chen DiffM 55 0 0 16 Apr 2025
Neural Guided Diffusion Bridges Gefan Yang Frank van der Meulen Stefan Sommer DiffM 65 0 0 17 Feb 2025
Learned Reference-based Diffusion Sampling for multi-modal distributions Maxence Noble Louis Grenioux Marylou Gabrié Alain Durmus DiffM 41 2 0 25 Oct 2024
How Discrete and Continuous Diffusion Meet: Comprehensive Analysis of Discrete Diffusion Models via a Stochastic Integral Framework Yinuo Ren Haoxuan Chen Grant M. Rotskoff Lexing Ying 52 3 0 04 Oct 2024
Stochastic Optimal Control for Diffusion Bridges in Function Spaces Byoungwoo Park Jungwon Choi Sungbin Lim Juho Lee 55 3 0 31 May 2024
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities Tara Akhound-Sadegh Jarrid Rector-Brooks A. Bose Sarthak Mittal Pablo Lemos ... Siamak Ravanbakhsh Gauthier Gidel Yoshua Bengio Nikolay Malkin Alexander Tong DiffM 45 42 0 09 Feb 2024
Implicit Diffusion: Efficient Optimization through Stochastic Sampling Pierre Marion Anna Korba Peter Bartlett Mathieu Blondel Valentin De Bortoli Arnaud Doucet Felipe Llinares-López Courtney Paquette Quentin Berthet 82 13 0 08 Feb 2024
Improved off-policy training of diffusion samplers Marcin Sendera Minsu Kim Sarthak Mittal Pablo Lemos Luca Scimeca Jarrid Rector-Brooks Alexandre Adam Yoshua Bengio Nikolay Malkin OffRL 71 18 0 07 Feb 2024
Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization Dinghuai Zhang Ricky Tian Qi Chen Cheng-Hao Liu Aaron C. Courville Yoshua Bengio 39 41 0 04 Oct 2023
Improved sampling via learned diffusions Lorenz Richter Julius Berner DiffM 39 52 0 03 Jul 2023
Nonlinear controllability and function representation by neural stochastic differential equations Tanya Veeravalli Maxim Raginsky DiffM 27 2 0 01 Dec 2022
An optimal control perspective on diffusion-based generative modeling Julius Berner Lorenz Richter Karen Ullrich DiffM 44 81 0 02 Nov 2022
Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow Xingchao Liu Chengyue Gong Qiang Liu OOD 63 859 0 07 Sep 2022
Let us Build Bridges: Understanding and Extending Diffusion Generative Models Xingchao Liu Lemeng Wu Mao Ye Qiang Liu DiffM 34 80 0 31 Aug 2022
Score-Guided Intermediate Layer Optimization: Fast Langevin Mixing for Inverse Problems Giannis Daras Y. Dagan A. Dimakis C. Daskalakis BDL 31 15 0 18 Jun 2022
Convergence for score-based generative modeling with polynomial complexity Holden Lee Jianfeng Lu Yixin Tan DiffM 24 127 0 13 Jun 2022
Neural Lagrangian Schrödinger Bridge: Diffusion Modeling for Population Dynamics Takeshi Koshizuka Issei Sato 39 6 0 11 Apr 2022
Path Integral Sampler: a stochastic control approach for sampling Qinsheng Zhang Yongxin Chen DiffM 18 101 0 30 Nov 2021
Schr{ö}dinger-F{ö}llmer Sampler: Sampling without Ergodicity Jian Huang Yuling Jiao Lican Kang Xu Liao Jin Liu Yanyan Liu 35 27 0 21 Jun 2021
Efficient and Accurate Gradients for Neural SDEs Patrick Kidger James Foster Xuechen Li Terry Lyons DiffM 24 60 0 27 May 2021
Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models Sam Bond-Taylor Adam Leach Yang Long Chris G. Willcocks VLM TPM 41 483 0 08 Mar 2021
Stein Variational Gradient Descent: many-particle and long-time asymptotics Nikolas Nusken D. M. Renger 29 22 0 25 Feb 2021
Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations Winnie Xu Ricky T. Q. Chen Xuechen Li David Duvenaud BDL UQCV 27 46 0 12 Feb 2021
Learning Continuous-Time Dynamics by Stochastic Differential Networks Yingru Liu Yucheng Xing Xuewen Yang Xin Wang Jing Shi Di Jin Zhaoyue Chen BDL 26 6 0 11 Jun 2020
Neural Controlled Differential Equations for Irregular Time Series Patrick Kidger James Morrill James Foster Terry Lyons AI4TS 27 451 0 18 May 2020
Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space Nikolas Nusken Lorenz Richter AI4CE 30 104 0 11 May 2020
Stochasticity in Neural ODEs: An Empirical Study V. Oganesyan Alexandra Volokhova Dmitry Vetrov BDL 30 20 0 22 Feb 2020
Scalable Gradients for Stochastic Differential Equations Xuechen Li Ting-Kam Leonard Wong Ricky T. Q. Chen David Duvenaud 17 310 0 05 Jan 2020