Learning Functional Priors and Posteriors from Data and Physics

Learning Functional Priors and Posteriors from Data and Physics

8 June 2021

George Karniadakis

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Papers citing "Learning Functional Priors and Posteriors from Data and Physics"

18 / 18 papers shown

Title
A Primer on Variational Inference for Physics-Informed Deep Generative Modelling Alex Glyn-Davies Arnaud Vadeboncoeur O. Deniz Akyildiz Ieva Kazlauskaite Mark Girolami PINN 110 0 0 10 Sep 2024
Generative Adversarial Networks Gilad Cohen Raja Giryes GAN 298 30,149 0 01 Mar 2022
Multi-fidelity Bayesian Neural Networks: Algorithms and Applications Xuhui Meng H. Babaee George Karniadakis 54 131 0 19 Dec 2020
All You Need is a Good Functional Prior for Bayesian Deep Learning Ba-Hien Tran Simone Rossi Dimitrios Milios Maurizio Filippone OOD BDL 63 61 0 25 Nov 2020
Meta-Learning in Neural Networks: A Survey Timothy M. Hospedales Antreas Antoniou P. Micaelli Amos Storkey OOD 395 1,988 0 11 Apr 2020
GAN-based Priors for Quantifying Uncertainty Dhruv V. Patel Assad A. Oberai BDL UQCV 43 7 0 27 Mar 2020
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 236 786 0 13 Mar 2020
tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware Junpeng Lao Christopher Suter I. Langmore C. Chimisov A. Saxena Pavel Sountsov Dave Moore Rif A. Saurous Matthew D. Hoffman Joshua V. Dillon 77 31 0 04 Feb 2020
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators Lu Lu Pengzhan Jin George Karniadakis 248 2,153 0 08 Oct 2019
Physics-informed semantic inpainting: Application to geostatistical modeling Q. Zheng L. Zeng Zhendan Cao George Karniadakis GAN 49 54 0 19 Sep 2019
Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations Siyu Dai Shawn Schaffert Andreas G. Hofmann 125 366 0 05 Nov 2018
Deep Neural Networks as Gaussian Processes Jaehoon Lee Yasaman Bahri Roman Novak S. Schoenholz Jeffrey Pennington Jascha Narain Sohl-Dickstein UQCV BDL 135 1,099 0 01 Nov 2017
Improved Training of Wasserstein GANs Ishaan Gulrajani Faruk Ahmed Martín Arjovsky Vincent Dumoulin Aaron Courville GAN 227 9,560 0 31 Mar 2017
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? Alex Kendall Y. Gal BDL OOD UD UQCV PER 362 4,719 0 15 Mar 2017
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 829 11,943 0 09 Mar 2017
Machine Learning of Linear Differential Equations using Gaussian Processes M. Raissi George Karniadakis 83 553 0 10 Jan 2017
Stochastic Gradient Hamiltonian Monte Carlo Tianqi Chen E. Fox Carlos Guestrin BDL 114 913 0 17 Feb 2014
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo Matthew D. Hoffman Andrew Gelman 171 4,309 0 18 Nov 2011