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Add OMGP results

arxiv-v3
Markus Kaiser 3 years ago
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efc56bc309
  1. 18
      dynamic_dirichlet_deep_gp.tex

18
dynamic_dirichlet_deep_gp.tex

@ -407,18 +407,19 @@ We use an implementation of DAGP in TensorFlow~\parencite{tensorflow2015-whitepa
For our model trained using the same setup, we report RMSE comparable to the previous results together with MLL.
Both are calculated based on a test set of 1000 equally spaced samples of the noiseless underlying function.
}%
\newcolumntype{H}{>{\setbox0=\hbox\bgroup}c<{\egroup}@{}}
\newcolumntype{Y}{>{\centering\arraybackslash}X}%
\newcolumntype{Z}{>{\columncolor{sStone!33}\centering\arraybackslash}X}%
\begin{tabularx}{\linewidth}{rY|YZZZZZZ}
\begin{tabularx}{\linewidth}{rYY|YYZZZZHZ}
\toprule
Outliers & DAGP & DAGP & CN & MDN & MLP & GPR & LGPR & RGPR \\
& \scriptsize MLL & \scriptsize RMSE & \scriptsize RMSE & \scriptsize RMSE & \scriptsize RMSE & \scriptsize RMSE & \scriptsize RMSE & \scriptsize RMSE \\
Outliers & DAGP & OMGP & DAGP & OMGP & CN & MDN & MLP & GPR & LGPR & RGPR \\
& \scriptsize MLL & \scriptsize MLL & \scriptsize RMSE & \scriptsize RMSE & \scriptsize RMSE & \scriptsize RMSE & \scriptsize RMSE & \scriptsize RMSE & \scriptsize RMSE & \scriptsize RMSE \\
\midrule
0\,\% & 2.86 & \textbf{0.008} & 0.034 & 0.028 & 0.039 & \textbf{0.008} & 0.022 & 0.017 \\
20\,\% & 2.71 & \textbf{0.008} & 0.022 & 0.087 & 0.413 & 0.280 & 0.206 & 0.013 \\
40\,\% & 2.12 & \textbf{0.005} & 0.018 & 0.565 & 0.452 & 0.447 & 0.439 & 1.322 \\
60\,\% & 0.874 & 0.031 & \textbf{0.023} & 0.645 & 0.636 & 0.602 & 0.579 & 0.738 \\
80\,\% & 0.126 & 0.128 & \textbf{0.084} & 0.778 & 0.829 & 0.779 & 0.777 & 1.523 \\
0\,\% & \textbf{2.86} & 2.09 & 0.008 & \textbf{0.005} & 0.034 & 0.028 & 0.039 & 0.008 & 0.022 & 0.017 \\
20\,\% & \textbf{2.71} & 1.83 & 0.008 & \textbf{0.005} & 0.022 & 0.087 & 0.413 & 0.280 & 0.206 & 0.013 \\
40\,\% & \textbf{2.12} & 1.60 & \textbf{0.005} & 0.007 & 0.018 & 0.565 & 0.452 & 0.447 & 0.439 & 1.322 \\
60\,\% & 0.874 & \textbf{1.23} & 0.031 & \textbf{0.006} & 0.023 & 0.645 & 0.636 & 0.602 & 0.579 & 0.738 \\
80\,\% & \textbf{0.126} & -1.35 & 0.128 & 0.896 & \textbf{0.084} & 0.778 & 0.829 & 0.779 & 0.777 & 1.523 \\
\bottomrule
\end{tabularx}
\\[.5\baselineskip]
@ -548,6 +549,7 @@ At $x = -10$ the inferred modes and assignment processes start reverting to thei
% 10 & DAGP 4 & 0.517 \pm 0.006 & 0.485 \pm 0.003 & 0.858 \pm 0.001 & 0.852 \pm 0.002 & 0.602 \pm 0.011 & 0.546 \pm 0.010 \\
% 10 & DAGP 5 & 0.535 \pm 0.004 & 0.506 \pm 0.005 & 0.851 \pm 0.003 & 0.851 \pm 0.003 & 0.662 \pm 0.009 & 0.581 \pm 0.012 \\
\addlinespace
10 & OMGP & -1.04 \pm 0.02 & -1.11 \pm 0.03 & 0.64 \pm 0.02 & 0.66 \pm 0.02 & -0.9 \pm 0.2 & -0.81 \pm 0.12 \\
10 & BNN+LV & 0.519 \pm 0.005 & \bfseries 0.524 \pm 0.005 & {\textemdash} & {\textemdash} & {\textemdash} & {\textemdash} \\
10 & GPR Mixed & 0.452 \pm 0.003 & 0.421 \pm 0.003 & {\textemdash} & {\textemdash} & {\textemdash} & {\textemdash} \\
10 & GPR Default & {\textemdash} & {\textemdash} & \bfseries 0.873 \pm 0.001 & \bfseries 0.867 \pm 0.001 & -7.01 \pm 0.11 & -7.54 \pm 0.14 \\

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