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Latent dynamics discovery is challenging in extracting complex dynamics from high-dimensional noisy neural data.
Gaussian process latent variable models for visualisation of high dimensional data
Neil D Lawrence · 2004
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Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity
M Yu Byron, John P Cunningham, Gopal Santhanam, Stephen I Ryu, Krishna V Shenoy, and Maneesh Sahani · 2009
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Variance stabilizing transformations of poisson, binomial and negative binomial distributions
Guan Yu · 2009
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Empirical models of spiking in neural populations
Jakob H Macke, Lars Buesing, John P Cunningham, M Yu Byron, Krishna V Shenoy, and Maneesh Sahani · 2011
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Decoding the activity of neuronal populations in macaque primary visual cortex
Arnulf BA Graf, Adam Kohn, Mehrdad Jazayeri, and J Anthony Movshon · 2011
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Stochastic variational inference
Matthew D Hoffman, David M Blei, Chong Wang, and John Paisley · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Variational gaussian process state-space models
Roger Frigola, Yutian Chen, and Carl Edward Rasmussen · 2014
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Collaborative multi-output gaussian processes
Trung V Nguyen, Edwin V Bonilla, et al · 2014
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Black box variational inference
Rajesh Ranganath, Sean Gerrish, and David Blei · 2014
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High-dimensional neural spike train analysis with generalized count linear dynamical systems
Yuanjun Gao, Lars Busing, Krishna V Shenoy, and John P Cunningham · 2015
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Poisson extension of gaussian process factor analysis for modeling spiking neural populations
Hooram Nam · 2015
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A recurrent latent variable model for sequential data
Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C Courville, and Yoshua Bengio · 2015
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Draw: A recurrent neural network for image generation
Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, and Daan Wierstra · 2015
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César Lincoln C Mattos, Zhenwen Dai, Andreas Damianou, Jeremy Forth, Guilherme A Barreto, and Neil D Lawrence · 2015
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Gaussian process based nonlinear latent structure discovery in multivariate spike train data
Anqi Wu, Nicholas G Roy, Stephen Keeley, and Jonathan W Pillow · 2017
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Structured inference networks for nonlinear state space models
Rahul G Krishnan, Uri Shalit, and David Sontag · 2017
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Identification of gaussian process state space models
Stefanos Eleftheriadis, Tom Nicholson, Marc Deisenroth, and James Hensman · 2017
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Deep probabilistic programming
Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, and David M. Blei · 2017
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Variational latent gaussian process for recovering single-trial dynamics from population spike trains
Yuan Zhao and Il Memming Park · 2017
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Bayesian learning and inference in recurrent switching linear dynamical systems
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Black box variational inference for state space models
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Linear dynamical neural population models through nonlinear embeddings
Yuanjun Gao, Evan W Archer, Liam Paninski, and John P Cunningham · 2016
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Pattern recognition and machine learning
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Computationally efficient bayesian learning of gaussian process state space models
Andreas Svensson, Arno Solin, Simo Särkkä, and Thomas Schön · 2016
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Variational inference for latent variables and uncertain inputs in gaussian processes
Andreas C Damianou, Michalis K Titsias, and Neil D Lawrence · 2016
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Scott Linderman, Matthew Johnson, Andrew Miller, Ryan Adams, David Blei, and Liam Paninski · 2017
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Inferring single-trial neural population dynamics using sequential auto-encoders
Chethan Pandarinath, Daniel J O’Shea, Jasmine Collins, Rafal Jozefowicz, Sergey D Stavisky, Jonathan C Kao, Eric M Trautmann, Matthew T Kaufman, Stephen I Ryu, Leigh R Hochberg, et al · 2018
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Reduced-rank linear dynamical systems
Qi She, Yuan Gao, Kai Xu, and Rosa HM Chan · 2018
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Stochastic dynamical systems based latent structure discovery in high-dimensional time series
Qi She and Rosa HM Chan · 2018
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