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Rename to Predictive Posterior

arxiv-v3
Markus Kaiser 3 years ago
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4424c50da3
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      dynamic_dirichlet_deep_gp.pdf
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      dynamic_dirichlet_deep_gp.tex

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dynamic_dirichlet_deep_gp.pdf

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dynamic_dirichlet_deep_gp.tex

@ -367,7 +367,7 @@ This extended bound thus has complexity $\Fun*{\Oh}{NM^2LK}$ to evaluate in the
% \setlength{\tabcolsep}{1pt}
\begin{tabularx}{\linewidth}{lYYYYYYYY}
\toprule
\resultrow{}{Bayesian}{Scalable Inference}{Approximate Joint}{Data Association}{Predictive Associations}{Multimodal Data}{Separate Models}{Interpretable Priors}
\resultrow{}{Bayesian}{Scalable Inference}{Predictive Posterior}{Data Association}{Predictive Associations}{Multimodal Data}{Separate Models}{Interpretable Priors}
\midrule
Experiment & & & & & \cref{tab:choicenet} & \cref{tab:cartpole} & \cref{fig:semi_bimodal} \\
\midrule
@ -388,7 +388,8 @@ This extended bound thus has complexity $\Fun*{\Oh}{NM^2LK}$ to evaluate in the
In this section we investigate the behavior of the DAGP model.
We use an implementation of DAGP in TensorFlow~\parencite{tensorflow2015-whitepaper} based on GPflow~\parencite{matthews_gpflow_2017} and the implementation of DSVI~\parencite{salimbeni_doubly_2017}.
\Cref{tab:model_capabilities} compares qualitative properties of DAGP and related work.
Both standard Gaussian process regression (GPR) and multi-layer perceptrons (MLP) do not impose structure which enables the model to handle multi-modal data.
All models can solve standard regression problems and yield unimodal predictive distributions or, in case of multi-layer perceptrons (MLP), a single point estimate.
Both standard Gaussian process regression (GPR) and MLP do not impose structure which enables the model to handle multi-modal data.
Mixture density networks (MDN)~\parencite{bishop_mixture_1994} and the infinite mixtures of Gaussian processes (RGPR)~\parencite{rasmussen_infinite_2002} model yield multi-modal posteriors through mixtures with many components but do not solve an association problem.
Similarly, Bayesian neural networks with added latent variables (BNN+LV)~\parencite{depeweg_learning_2016} represent such a mixture through a continuous latent variable.
Both the overlapping mixtures of Gaussian processes (OMGP)~\parencite{lazaro-gredilla_overlapping_2012} model and DAGP explicitly model the data association problem and yield independent models for the different generating processes.

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