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Uncertainty quantification of neural network models of evolving processes via Langevin sampling

21 April 2025

Papers citing "Uncertainty quantification of neural network models of evolving processes via Langevin sampling"

28 / 28 papers shown

Title
Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks G. A. Padmanabha Cosmin Safta N. Bouklas Reese E. Jones 82 2 0 21 Dec 2024
Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models G. A. Padmanabha J. Fuhg Cosmin Safta Reese E. Jones N. Bouklas 68 5 0 30 Jun 2024
Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processes J. Hauth Cosmin Safta Xun Huan Ravi G. Patel Reese E. Jones BDL UQCV 57 2 0 17 Feb 2024
Score-based Diffusion Models via Stochastic Differential Equations -- a Technical Tutorial Wenpin Tang Hanyang Zhao DiffM 84 27 0 12 Feb 2024
FreeU: Free Lunch in Diffusion U-Net Chenyang Si Ziqi Huang Yuming Jiang Ziwei Liu DiffM 80 145 0 20 Sep 2023
A Brief Review of Hypernetworks in Deep Learning Vinod Kumar Chauhan Jiandong Zhou Ping Lu Soheila Molaei David Clifton 96 106 0 12 Jun 2023
Score Operator Newton transport N. Chandramoorthy F. Schaefer Youssef Marzouk OT 52 1 0 16 May 2023
Stochastic Interpolants: A Unifying Framework for Flows and Diffusions M. S. Albergo Nicholas M. Boffi Eric Vanden-Eijnden DiffM 299 323 0 15 Mar 2023
Hypernetworks in Meta-Reinforcement Learning Jacob Beck Matthew Jackson Risto Vuorio Shimon Whiteson OffRL 76 30 0 20 Oct 2022
Flow Matching for Generative Modeling Y. Lipman Ricky T. Q. Chen Heli Ben-Hamu Maximilian Nickel Matt Le OOD 207 1,365 0 06 Oct 2022
Equinox: neural networks in JAX via callable PyTrees and filtered transformations Patrick Kidger Cristian Garcia 56 126 0 30 Oct 2021
Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations Winnie Xu Ricky T. Q. Chen Xuechen Li David Duvenaud BDL UQCV 68 49 0 12 Feb 2021
Maximum Likelihood Training of Score-Based Diffusion Models Yang Song Conor Durkan Iain Murray Stefano Ermon DiffM 160 670 0 22 Jan 2021
Bayesian Neural Ordinary Differential Equations Raj Dandekar Karen Chung Vaibhav Dixit Mohamed Tarek Aslan Garcia-Valadez Krishna Vishal Vemula Chris Rackauckas UQCV OOD BDL 43 53 0 14 Dec 2020
Score-Based Generative Modeling through Stochastic Differential Equations Yang Song Jascha Narain Sohl-Dickstein Diederik P. Kingma Abhishek Kumar Stefano Ermon Ben Poole DiffM SyDa 350 6,551 0 26 Nov 2020
Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit Belinda Tzen Maxim Raginsky DiffM 164 210 0 23 May 2019
Augmented Neural ODEs Emilien Dupont Arnaud Doucet Yee Whye Teh BDL 150 631 0 02 Apr 2019
Graph HyperNetworks for Neural Architecture Search Chris Zhang Mengye Ren R. Urtasun GNN 68 280 0 12 Oct 2018
Neural Ordinary Differential Equations T. Chen Yulia Rubanova J. Bettencourt David Duvenaud AI4CE 417 5,156 0 19 Jun 2018
Stochastic Hyperparameter Optimization through Hypernetworks Jonathan Lorraine David Duvenaud 74 140 0 26 Feb 2018
Bayesian Hypernetworks David M. Krueger Chin-Wei Huang Riashat Islam Ryan Turner Alexandre Lacoste Aaron Courville UQCV BDL 70 139 0 13 Oct 2017
HyperNetworks David R Ha Andrew M. Dai Quoc V. Le 164 1,632 0 27 Sep 2016
Input Convex Neural Networks Brandon Amos Lei Xu J. Zico Kolter 280 623 0 22 Sep 2016
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Qiang Liu Dilin Wang BDL 73 1,093 0 16 Aug 2016
Variational Inference: A Review for Statisticians David M. Blei A. Kucukelbir Jon D. McAuliffe BDL 287 4,807 0 04 Jan 2016
U-Net: Convolutional Networks for Biomedical Image Segmentation Olaf Ronneberger Philipp Fischer Thomas Brox SSeg 3DV 1.9K 77,341 0 18 May 2015
Black Box Variational Inference Rajesh Ranganath S. Gerrish David M. Blei DRL BDL 144 1,168 0 31 Dec 2013
Measuring and testing dependence by correlation of distances G. Székely Maria L. Rizzo N. K. Bakirov 279 2,602 0 28 Mar 2008