Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

23 May 2019

Papers citing "Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit"

29 / 29 papers shown

Title
Efficient Training of Neural SDEs Using Stochastic Optimal Control Rembert Daems Manfred Opper Guillaume Crevecoeur Tolga Birdal 36 0 0 22 May 2025
Uncertainty quantification of neural network models of evolving processes via Langevin sampling Cosmin Safta Reese E. Jones Ravi G. Patel Raelynn Wonnacot Dan S. Bolintineanu Craig M. Hamel S. Kramer BDL 76 0 0 21 Apr 2025
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 85 1 0 16 Apr 2025
Neural ODE Transformers: Analyzing Internal Dynamics and Adaptive Fine-tuning Anh Tong Thanh Nguyen-Tang Dongeun Lee Duc Nguyen Toan M. Tran David Hall Cheongwoong Kang Jaesik Choi 90 1 0 03 Mar 2025
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 91 7 0 10 Jan 2025
Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series Byoungwoo Park Hyungi Lee Juho Lee AI4TS 101 0 0 08 Oct 2024
Efficient Training of Neural Stochastic Differential Equations by Matching Finite Dimensional Distributions Jianxin Zhang Josh Viktorov Doosan Jung Emily Pitler DiffM 138 0 0 04 Oct 2024
Adaptive teachers for amortized samplers Minsu Kim Sanghyeok Choi Taeyoung Yun Emmanuel Bengio Leo Feng Jarrid Rector-Brooks Sungsoo Ahn Jinkyoo Park Nikolay Malkin Yoshua Bengio 382 6 0 02 Oct 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 86 22 0 07 Feb 2024
Demystifying Variational Diffusion Models Fabio De Sousa Ribeiro Ben Glocker DiffM 50 0 0 11 Jan 2024
Large-time asymptotics in deep learning Carlos Esteve Borjan Geshkovski Dario Pighin Enrique Zuazua 77 34 0 06 Aug 2020
Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise Xuanqing Liu Tesi Xiao Si Si Qin Cao Sanjiv Kumar Cho-Jui Hsieh 53 134 0 05 Jun 2019
Neural Jump Stochastic Differential Equations Junteng Jia Austin R. Benson BDL 47 222 0 24 May 2019
Theoretical guarantees for sampling and inference in generative models with latent diffusions Belinda Tzen Maxim Raginsky DiffM 55 99 0 05 Mar 2019
DiffEqFlux.jl - A Julia Library for Neural Differential Equations Christopher Rackauckas Mike Innes Yingbo Ma J. Bettencourt Lyndon White Vaibhav Dixit 38 116 0 06 Feb 2019
Convergence of the Deep BSDE Method for Coupled FBSDEs Jiequn Han Jihao Long 45 158 0 03 Nov 2018
Deep learning with differential Gaussian process flows Pashupati Hegde Markus Heinonen Harri Lähdesmäki Samuel Kaski BDL 51 42 0 09 Oct 2018
Adaptive Path-Integral Autoencoder: Representation Learning and Planning for Dynamical Systems Jung-Su Ha Young-Jin Park Hyeok-Joo Chae Soon-Seo Park Han-Lim Choi BDL 43 26 0 05 Jul 2018
Neural Ordinary Differential Equations T. Chen Yulia Rubanova J. Bettencourt David Duvenaud AI4CE 232 5,024 0 19 Jun 2018
Black-box Variational Inference for Stochastic Differential Equations Tom Ryder Andrew Golightly A. Mcgough D. Prangle 60 57 0 09 Feb 2018
Maximum Principle Based Algorithms for Deep Learning Qianxiao Li Long Chen Cheng Tai E. Weinan 47 222 0 26 Oct 2017
Continuous-Time Flows for Efficient Inference and Density Estimation Changyou Chen Chunyuan Li Liquan Chen Wenlin Wang Yunchen Pu Lawrence Carin TPM 62 57 0 04 Sep 2017
Stable Architectures for Deep Neural Networks E. Haber Lars Ruthotto 75 725 0 09 May 2017
Identity Matters in Deep Learning Moritz Hardt Tengyu Ma OOD 61 399 0 14 Nov 2016
A probabilistic model for the numerical solution of initial value problems Michael Schober Simo Särkkä Philipp Hennig 28 81 0 17 Oct 2016
Variational Inference: A Review for Statisticians David M. Blei A. Kucukelbir Jon D. McAuliffe BDL 183 4,748 0 04 Jan 2016
Automatic differentiation in machine learning: a survey A. G. Baydin Barak A. Pearlmutter Alexey Radul J. Siskind PINN AI4CE ODL 132 2,775 0 20 Feb 2015
Black Box Variational Inference Rajesh Ranganath S. Gerrish David M. Blei DRL BDL 72 1,157 0 31 Dec 2013
Auto-Encoding Variational Bayes Diederik P. Kingma Max Welling BDL 367 16,962 0 20 Dec 2013