Browse Source

Rename to Data Association with GPs

icml
Markus Kaiser 3 years ago
parent
commit
52d2885b63
  1. 10
      dynamic_dirichlet_deep_gp.tex

10
dynamic_dirichlet_deep_gp.tex

@ -18,13 +18,13 @@
\addbibresource{additional.bib}
% We set this for hyperref
\title{Data Association using Gaussian Processes}
\title{Data Association with Gaussian Processes}
% \author{\href{mailto:markus.kaiser@siemens.com}{Markus Kaiser}}
\author{Anonymous}
\begin{document}
\twocolumn[
\icmltitle{Data Association using Gaussian Processes}
\icmltitle{Data Association with Gaussian Processes}
% NOTE(mrksr): We leave anonymous information here for now.
\begin{icmlauthorlist}
@ -93,12 +93,12 @@ We describe this non-stationary structure using additional Gaussian process prio
This leads to a flexible yet interpretable model with a principled treatment of uncertainty.
The paper has the following contributions:
In \cref{sec:model}, we propose the data association using Gaussian Processes model (DAGP).
In \cref{sec:model}, we propose the data association with Gaussian Processes model (DAGP).
In \cref{sec:variational_approximation}, we present an efficient learning scheme via a variational approximation which allows us to simultaneously train all parts of our model via stochastic optimization and show how the same learning scheme can be applied to deep Gaussian process priors.
We demonstrate our model on a noise separation problem, an artificial multimodal data set, and a multi-regime regression problem based on the cart-pole benchmark in \cref{sec:experiments}.
\section{Data Association using Gaussian Processes}
\section{Data Association with Gaussian Processes}
\label{sec:model}
\begin{figure}[t]
\centering
@ -113,7 +113,7 @@ We demonstrate our model on a noise separation problem, an artificial multimodal
The different parts of the model are determined by the yellow hyperparameters and blue variational parameters.
}
\end{figure}
The data association using Gaussian Processes (DAGP) model assumes that there exist $K$ independent functions $\Set*{f^{\pix{k}}}_{k=1}^K$, which generate pairs of observations $\D = \Set*{(\mat{x_n}, \mat{y_n})}_{n=1}^N$.
The data association with Gaussian Processes (DAGP) model assumes that there exist $K$ independent functions $\Set*{f^{\pix{k}}}_{k=1}^K$, which generate pairs of observations $\D = \Set*{(\mat{x_n}, \mat{y_n})}_{n=1}^N$.
Each data point is generated by evaluating one of the $K$ latent functions and adding Gaussian noise from a corresponding likelihood.
The assignment of the $\nth{n}$ data point to one of the functions is specified by the indicator vector $\mat{a_n} \in \Set*{0, 1}^K$, which has exactly one non-zero entry.
Our goal is to formulate simultaneous Bayesian inference on the functions $f^{\pix{k}}$ and the assignments $\mat{a_n}$.

Loading…
Cancel
Save