Auto-Differentiating Linear Algebra

Auto-Differentiating Linear Algebra

24 October 2017

Matthias Seeger

Neil D. Lawrence

Papers citing "Auto-Differentiating Linear Algebra"

13 / 13 papers shown

Title
Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models Jinlin Lai Justin Domke Daniel Sheldon 66 0 0 31 Oct 2024
On Correctness of Automatic Differentiation for Non-Differentiable Functions Wonyeol Lee Hangyeol Yu Xavier Rival Hongseok Yang 40 40 0 12 Jun 2020
Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Start Valerio Perrone Rodolphe Jenatton Matthias Seeger Cédric Archambeau BDL 25 24 0 08 Dec 2017
GPflow: A Gaussian process library using TensorFlow A. G. Matthews Mark van der Wilk T. Nickson Keisuke Fujii A. Boukouvalas Pablo León-Villagrá Zoubin Ghahramani J. Hensman GP 58 662 0 27 Oct 2016
Differentiation of the Cholesky decomposition Iain Murray 39 37 0 24 Feb 2016
Variational Auto-encoded Deep Gaussian Processes Zhenwen Dai Andreas C. Damianou Javier I. González Neil D. Lawrence BDL 37 131 0 19 Nov 2015
Deep Kernel Learning A. Wilson Zhiting Hu Ruslan Salakhutdinov Eric Xing BDL 167 882 0 06 Nov 2015
Generative Moment Matching Networks Yujia Li Kevin Swersky R. Zemel OOD GAN 83 844 0 10 Feb 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 626 149,474 0 22 Dec 2014
Stochastic Backpropagation and Approximate Inference in Deep Generative Models Danilo Jimenez Rezende S. Mohamed Daan Wierstra BDL 49 139 0 16 Jan 2014
Auto-Encoding Variational Bayes Diederik P. Kingma Max Welling BDL 336 16,972 0 20 Dec 2013
Deep Generative Stochastic Networks Trainable by Backprop Yoshua Bengio Eric Thibodeau-Laufer Guillaume Alain J. Yosinski BDL 97 396 0 05 Jun 2013
Deep Gaussian Processes Andreas C. Damianou Neil D. Lawrence GP BDL 62 1,178 0 02 Nov 2012