Efficient non-conjugate Gaussian process factor models for spike count
data using polynomial approximations

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Efficient non-conjugate Gaussian process factor models for spike count data using polynomial approximations

7 June 2019

Stephen L. Keeley

Jonathan W. Pillow

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Papers citing "Efficient non-conjugate Gaussian process factor models for spike count data using polynomial approximations"

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Title
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models Hugh Salimbeni Stefanos Eleftheriadis J. Hensman BDL 86 86 0 24 Mar 2018
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference Jonathan H. Huggins Ryan P. Adams Tamara Broderick 93 33 0 26 Sep 2017
Bayesian latent structure discovery from multi-neuron recordings Scott W. Linderman Ryan P. Adams Jonathan W. Pillow 50 54 0 26 Oct 2016
Variational Latent Gaussian Process for Recovering Single-Trial Dynamics from Population Spike Trains Yuan Zhao Il-Su Park 91 117 0 11 Apr 2016
Black box variational inference for state space models Evan Archer Il Memming Park Lars Buesing John P. Cunningham Liam Paninski BDL 83 161 0 23 Nov 2015
Variational Dropout and the Local Reparameterization Trick Diederik P. Kingma Tim Salimans Max Welling BDL 236 1,519 0 08 Jun 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 2.2K 150,501 0 22 Dec 2014
Black Box Variational Inference Rajesh Ranganath S. Gerrish David M. Blei DRL BDL 158 1,168 0 31 Dec 2013
Stochastic Variational Inference Matt Hoffman David M. Blei Chong-Jun Wang John Paisley BDL 280 2,629 0 29 Jun 2012