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Generic Inference in Latent Gaussian Process Models
[article]
2018
arXiv
pre-print
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We develop an automated variational method for inference in models with Gaussian process (GP) priors and general likelihoods. ...
Using a mixture of Gaussians as the variational distribution, we show that the evidence lower bound and its gradients can be estimated efficiently using samples from univariate Gaussian distributions. ...
South Wales (unsw sydney) and was partially supported by unsw's Faculty of Engineering Research Grant Program project # PS37866; unsw's Academic Start-Up Funding Scheme project # PS41327; and an aws in ...
arXiv:1609.00577v2
fatcat:ngpjezdvv5egheozyzc2pr3qya
Warped Mixtures for Nonparametric Cluster Shapes
[article]
2014
arXiv
pre-print
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To produce more appropriate clusterings, we introduce a model which warps a latent mixture of Gaussians to produce nonparametric cluster shapes. ...
We derive a simple inference scheme for this model which analytically integrates out both the mixture parameters and the warping function. ...
In addition, the iWMM assumes that the latent coordinates are generated from a Dirichlet process mixture model. ...
arXiv:1408.2061v1
fatcat:b5svigdi3jhxjdq7zcgpc6ehs4
The Variational Gaussian Process
[article]
2016
arXiv
pre-print
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The VGP achieves new state-of-the-art results for unsupervised learning, inferring models such as the deep latent Gaussian model and the recently proposed DRAW. ...
Variational inference is a powerful tool for approximate inference, and it has been recently applied for representation learning with deep generative models. ...
A SPECIAL CASES OF THE VARIATIONAL GAUSSIAN PROCESS We now analyze two special cases of the VGP: by limiting its generative process in various ways, we recover well-known models. ...
arXiv:1511.06499v4
fatcat:rxtue3ahoveytj5im3ubye5vyu
Warped Mixtures for Nonparametric Cluster Shapes
[article]
2013
arXiv
pre-print
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To produce more appropriate clusterings, we introduce a model which warps a latent mixture of Gaussians to produce nonparametric cluster shapes. ...
We derive a simple inference scheme for this model which analytically integrates out both the mixture parameters and the warping function. ...
In addition, the iWMM assumes that the latent coordinates are generated from a Dirichlet process mixture model. ...
arXiv:1206.1846v2
fatcat:vgq276gmuzhnjmc55ofbnrko2i
Variational Latent Gaussian Process for Recovering Single-Trial Dynamics from Population Spike Trains
2017
Neural Computation
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2016 pre-print arXiv:1604.03053v4 fatcat:pdm2q73kbzaebg6prq3brg622uOther Versions
Here, we propose a practical and efficient inference method, called the variational latent Gaussian process (vLGP). ...
The vLGP combines a generative model with a history-dependent point process observation together with a smoothness prior on the latent trajectories. ...
Therefore, we propose to relax this modeling assumption and impose a general Gaussian process prior to nonparametrically infer the latent dynamics, similar to the Gaussian process factor analysis (GPFA ...
doi:10.1162/neco_a_00953
pmid:28333587
fatcat:ac4cktpxxzdnni4p57wapmjewq
Point process latent variable models of larval zebrafish behavior
2018
Neural Information Processing Systems
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We incorporate these variables as latent marks of a point process and explore various models for their dynamics. ...
To infer the latent variables and fit the parameters of this model, we develop an amortized variational inference algorithm that targets the collapsed posterior distribution, analytically marginalizing ...
The class of Gaussian process-modulated point processes are well-studied in statistics and machine learning more generally. ...
dblp:conf/nips/SharmaJEL18
fatcat:wjkyzukogze6fg5h6l36ztdyoa
Joint Distribution across Representation Space for Out-of-Distribution Detection
[article]
2021
arXiv
pre-print
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Specifically, We construct a generative model, called Latent Sequential Gaussian Mixture (LSGM), to depict how the in-distribution latent features are generated in terms of the trace of DNN inference across ...
We first construct the Gaussian Mixture Model (GMM) based on in-distribution latent features for each hidden layer, and then connect GMMs via the transition probabilities of the inference traces. ...
Conclusions In this paper, we propose a generative probabilistic graphical model across representation spaces, Latent Sequential Gaussian Mixture, to depict the process of DNN inference. ...
arXiv:2103.12344v2
fatcat:hii5ttcw65e3nmjdurhazmaswq
Automated Variational Inference for Gaussian Process Models
2014
Neural Information Processing Systems
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We develop an automated variational method for approximate inference in Gaussian process (GP) models whose posteriors are often intractable. ...
Our method can be a valuable tool for practitioners and researchers to investigate new models with minimal effort in deriving model-specific inference algorithms. ...
Discussion We have developed automated variational inference for Gaussian process models (AGP). ...
dblp:conf/nips/NguyenB14
fatcat:apfqukc2l5gjta4jfeptvrpzsu
The Gaussian Process Density Sampler
2008
Neural Information Processing Systems
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We present the Gaussian Process Density Sampler (GPDS), an exchangeable generative model for use in nonparametric Bayesian density estimation. ...
We can also infer the hyperparameters of the Gaussian process. We compare this density modeling technique to several existing techniques on a toy problem and a skullreconstruction task. ...
Introduction We present the Gaussian Process Density Sampler (GPDS), a generative model for probability density functions, based on a Gaussian process. ...
dblp:conf/nips/AdamsMM08
fatcat:kdfjeallbbdlnahefcuyxyhd5e
Temporal alignment and latent Gaussian process factor inference in population spike trains
[article]
2018
bioRxiv
pre-print
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Our approach is based on shared latent Gaussian processes (GPs) which are combined linearly, as in the Gaussian Process Factor Analysis (GPFA) algorithm. ...
We extend GPFA to handle unbinned spike-train data by incorporating a continuous time point-process likelihood model, achieving scalability with a sparse variational approximation. ...
Temporal alignment and latent factor inference using Gaussian processes The svGPFA model we have developed in section 3 aims to extract different latent trajectories on each trial. ...
doi:10.1101/331751
fatcat:nxaijfww7fbpfakfijytgkaxo4
Structured Bayesian Gaussian process latent variable model
[article]
2018
arXiv
pre-print
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We introduce a Bayesian Gaussian process latent variable model that explicitly captures spatial correlations in data using a parameterized spatial kernel and leveraging structure-exploiting algebra on ...
Modeling high-dimensional time series systems is enabled through use of a dynamical GP latent variable prior. ...
Bayesian generative models such as the Gaussian process latent variable model [2, 3, 1] and the related unsupervised deep Gaussian processes [4, 5] leverage the expressive, yet regularized flexibility ...
arXiv:1805.08665v1
fatcat:fftbpnsavrdsdfzblpnplclkcu
Kernel Topic Models
[article]
2011
arXiv
pre-print
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The KTM can also be interpreted as a type of Gaussian process latent variable model, or as a topic model conditional on document features, uncovering links between earlier work in these areas. ...
The main challenge is efficient approximate inference on the latent Gaussian. We present an approximate algorithm cast around a Laplace approximation in a transformed basis. ...
GPLVMs learn mappings from data-space to a lower-dimensional space, assuming the generative model for the data in the latent space is a Gaussian process. ...
arXiv:1110.4713v1
fatcat:klwrebjkqvakxo6n24d7iccudi
Semi-supervised Gaussian process latent variable model with pairwise constraints
2010
Neurocomputing
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+ Informative Vector Machine [Lawrence 2004] • Introduction to Gaussian Processes Interlude • Gaussian Process Latent Variable Models • Gaussian Process Dynamical Models Observation model: GPLVM for the ...
Variable Models
• Gaussian Process Dynamical Models
-Application to motion capture data
[Lawrence, 2004; 2005]
16
Gaussian Process
Latent Variable Models
Observations (output):
Latent variables ...
doi:10.1016/j.neucom.2010.01.021
fatcat:p7aelt5rtjeqfdfddy7sygcase
Scalable Inference for Gaussian Process Models with Black-Box Likelihoods
2015
Neural Information Processing Systems
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We propose a sparse method for scalable automated variational inference (AVI) in a large class of models with Gaussian process (GP) priors, multiple latent functions, multiple outputs and non-linear likelihoods ...
Gaussians. ...
Acknowledgments This work has been partially supported by UNSW's Faculty of Engineering Research Grant Program project # PS37866 and an AWS in Education Research Grant award. ...
dblp:conf/nips/DezfouliB15
fatcat:fhx5627irzdm7imeppau36lcry
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data
[article]
2015
arXiv
pre-print
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Our model ties together many existing models, linking the linear categorical latent Gaussian model, the Gaussian process latent variable model, and Gaussian process classification. ...
We derive inference for our model based on recent developments in sampling based variational inference. ...
Gaussian process latent variable model (top to bottom, Lawrence (2005) ). ...
arXiv:1503.02182v1
fatcat:yu4k5o5tujbu5lacf6wb5kx6cq
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