v1v2 (latest)

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

Annual Conference Computational Learning Theory (COLT), 2019

5 March 2019

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

29 / 79 papers shown

Title
Normalizing flow neural networks by JKO schemeNeural Information Processing Systems (NeurIPS), 2022 Chen Xu Xiuyuan Cheng Yao Xie 374 36 0 29 Dec 2022
Fast Point Cloud Generation with Straight FlowsComputer Vision and Pattern Recognition (CVPR), 2022 Lemeng Wu Dilin Wang Chengyue Gong Xingchao Liu Yunyang Xiong Rakesh Ranjan Raghuraman Krishnamoorthi Vikas Chandra Qiang Liu 257 56 0 04 Dec 2022
Nonlinear controllability and function representation by neural stochastic differential equationsConference on Learning for Dynamics & Control (L4DC), 2022 Tanya Veeravalli Maxim Raginsky DiffM 116 2 0 01 Dec 2022
An optimal control perspective on diffusion-based generative modeling Julius Berner Lorenz Richter Karen Ullrich DiffM 407 125 0 02 Nov 2022
Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified FlowInternational Conference on Learning Representations (ICLR), 2022 Xingchao Liu Chengyue Gong Qiang Liu OOD 934 1,916 0 07 Sep 2022
Let us Build Bridges: Understanding and Extending Diffusion Generative Models Xingchao Liu Lemeng Wu Mao Ye Qiang Liu DiffM 192 97 0 31 Aug 2022
Score-Guided Intermediate Layer Optimization: Fast Langevin Mixing for Inverse Problems Giannis Daras Y. Dagan A. Dimakis C. Daskalakis BDL 295 16 0 18 Jun 2022
Convergence for score-based generative modeling with polynomial complexityNeural Information Processing Systems (NeurIPS), 2022 Holden Lee Jianfeng Lu Yixin Tan DiffM 217 171 0 13 Jun 2022
Neural Lagrangian Schrödinger Bridge: Diffusion Modeling for Population DynamicsInternational Conference on Learning Representations (ICLR), 2022 Takeshi Koshizuka Issei Sato 308 7 0 11 Apr 2022
Fractional SDE-Net: Generation of Time Series Data with Long-term MemoryInternational Conference on Data Science and Advanced Analytics (DSAA), 2022 Kunihiko Miyoshi Kei Nakagawa AI4TS 159 11 0 16 Jan 2022
Path Integral Sampler: a stochastic control approach for sampling Qinsheng Zhang Yongxin Chen DiffM 271 155 0 30 Nov 2021
Bayesian Learning via Neural Schrödinger-Föllmer FlowsStatistics and computing (Stat Comput), 2021 Francisco Vargas Andrius Ovsianas David Fernandes Mark Girolami Neil D. Lawrence Nikolas Nusken BDL 787 57 0 20 Nov 2021
Convergence Analysis of Schr{ö}dinger-F{ö}llmer Sampler without Convexity Yuling Jiao Lican Kang Yanyan Liu Youzhou Zhou OT 109 7 0 10 Jul 2021
Schr{ö}dinger-F{ö}llmer Sampler: Sampling without ErgodicityIEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2021 Jian Huang Yuling Jiao Lican Kang Xu Liao Jin Liu Yanyan Liu 223 27 0 21 Jun 2021
Diffusion Schrödinger Bridge with Applications to Score-Based Generative ModelingNeural Information Processing Systems (NeurIPS), 2021 Valentin De Bortoli James Thornton J. Heng Arnaud Doucet DiffM OT 511 591 0 01 Jun 2021
Efficient and Accurate Gradients for Neural SDEsNeural Information Processing Systems (NeurIPS), 2021 Patrick Kidger James Foster Xuechen Li Terry Lyons DiffM 345 80 0 27 May 2021
Neural Options Pricing Timothy C DeLise 82 2 0 27 May 2021
Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive ModelsIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021 Sam Bond-Taylor Adam Leach Yang Long Chris G. Willcocks VLM TPM 693 621 0 08 Mar 2021
Stein Variational Gradient Descent: many-particle and long-time asymptoticsFoundations of Data Science (FODS), 2021 Nikolas Nusken D. M. Renger 203 25 0 25 Feb 2021
Infinitely Deep Bayesian Neural Networks with Stochastic Differential EquationsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2021 Winnie Xu Ricky T. Q. Chen Xuechen Li David Duvenaud BDL UQCV 279 52 0 12 Feb 2021
Neural SDEs as Infinite-Dimensional GANsInternational Conference on Machine Learning (ICML), 2021 Patrick Kidger James Foster Xuechen Li Harald Oberhauser Terry Lyons DiffM 323 191 0 06 Feb 2021
Generative Ensemble Regression: Learning Particle Dynamics from Observations of Ensembles with Physics-Informed Deep Generative Models Liu Yang C. Daskalakis George Karniadakis 148 12 0 05 Aug 2020
Estimating Stochastic Poisson Intensities Using Deep Latent ModelsOnline World Conference on Soft Computing in Industrial Applications (WSCIA), 2020 Ruixin Wang Prateek Jaiswal Harsha Honnappa 161 7 0 12 Jul 2020
Learning Continuous-Time Dynamics by Stochastic Differential Networks Yingru Liu Yucheng Xing Xuewen Yang Xin Wang Jing Shi Di Jin Zhaoyue Chen BDL 197 6 0 11 Jun 2020
Neural Controlled Differential Equations for Irregular Time Series Patrick Kidger James Morrill James Foster Terry Lyons AI4TS 437 603 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 238 140 0 11 May 2020
Stochasticity in Neural ODEs: An Empirical StudyInternational Conference on Learning Representations (ICLR), 2020 V. Oganesyan Alexandra Volokhova Dmitry Vetrov BDL 171 21 0 22 Feb 2020
Schrödinger Bridge Samplers Espen Bernton J. Heng Arnaud Doucet Pierre E. Jacob 139 28 0 31 Dec 2019
Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit Belinda Tzen Maxim Raginsky DiffM 375 238 0 23 May 2019