Bayesian Neural Ordinary Differential Equations

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Bayesian Neural Ordinary Differential Equations

14 December 2020

Aslan Garcia-Valadez

Krishna Vishal Vemula

Chris Rackauckas

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Papers citing "Bayesian Neural Ordinary Differential Equations"

19 / 19 papers shown

Title
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 109 0 0 21 Apr 2025
Beyond Predictions in Neural ODEs: Identification and Interventions H. Aliee Fabian J. Theis Niki Kilbertus CML 101 25 0 23 Jun 2021
Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users Laurent Valentin Jospin Wray Buntine F. Boussaïd Hamid Laga Bennamoun OOD BDL UQCV 86 629 0 14 Jul 2020
Bayesian Hidden Physics Models: Uncertainty Quantification for Discovery of Nonlinear Partial Differential Operators from Data Steven Atkinson 40 8 0 07 Jun 2020
Universal Differential Equations for Scientific Machine Learning Christopher Rackauckas Yingbo Ma Julius Martensen Collin Warner K. Zubov R. Supekar Dominic J. Skinner Ali Ramadhan Alan Edelman AI4CE 91 597 0 13 Jan 2020
Differential Bayesian Neural Nets Andreas Look M. Kandemir BDL UQCV 42 8 0 02 Dec 2019
Subspace Inference for Bayesian Deep Learning Pavel Izmailov Wesley J. Maddox Polina Kirichenko T. Garipov Dmitry Vetrov A. Wilson UQCV BDL 75 144 0 17 Jul 2019
ODE $^2$ VAE: Deep generative second order ODEs with Bayesian neural networks Çağatay Yıldız Markus Heinonen Harri Lähdesmäki BDL DRL 71 88 0 27 May 2019
A Simple Baseline for Bayesian Uncertainty in Deep Learning Wesley J. Maddox T. Garipov Pavel Izmailov Dmitry Vetrov A. Wilson BDL UQCV 93 808 0 07 Feb 2019
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference Kumar Shridhar F. Laumann Marcus Liwicki BDL UQCV 83 177 0 08 Jan 2019
Neural Ordinary Differential Equations T. Chen Yulia Rubanova J. Bettencourt David Duvenaud AI4CE 417 5,156 0 19 Jun 2018
Learning Model Reparametrizations: Implicit Variational Inference by Fitting MCMC distributions Michalis K. Titsias BDL 48 23 0 04 Aug 2017
Reinterpreting Importance-Weighted Autoencoders Chris Cremer Q. Morris David Duvenaud BDL FAtt 111 94 0 10 Apr 2017
Variational Inference with Normalizing Flows Danilo Jimenez Rezende S. Mohamed DRL BDL 318 4,197 0 21 May 2015
Stochastic Gradient Hamiltonian Monte Carlo Tianqi Chen E. Fox Carlos Guestrin BDL 109 913 0 17 Feb 2014
Black Box Variational Inference Rajesh Ranganath S. Gerrish David M. Blei DRL BDL 150 1,168 0 31 Dec 2013
Auto-Encoding Variational Bayes Diederik P. Kingma Max Welling BDL 455 16,923 0 20 Dec 2013
Stochastic Variational Inference Matt Hoffman David M. Blei Chong-Jun Wang John Paisley BDL 262 2,627 0 29 Jun 2012
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo Matthew D. Hoffman Andrew Gelman 169 4,309 0 18 Nov 2011