### Remove leftover starred floats

arxiv-v3
Markus Kaiser 3 years ago
parent
commit
f362d0a49f
1 changed files with 5 additions and 5 deletions
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dynamic_dirichlet_deep_gp.tex

#### 10 dynamic_dirichlet_deep_gp.tex View File

 @ -397,7 +397,7 @@ We use an implementation of DAGP in TensorFlow~\parencite{tensorflow2015-whitepa \subsection{Noise Separation} \label{subsec:choicenet} % \begin{figure*}[t] \begin{figure}[t]  \centering  \captionof{table}{  \label{tab:choicenet} @ -453,7 +453,7 @@ We use an implementation of DAGP in TensorFlow~\parencite{tensorflow2015-whitepa  The bimodal DAGP identifies the signal perfectly up to 40\,\% outliers.  For 60\,\% outliers, some of the noise is interpreted as signal, but the latent function is still recovered.  } \end{figure*} \end{figure} % We begin with an experiment based on a noise separation problem. We apply DAGP to a one-dimensional regression problem with uniformly distributed asymmetric outliers in the training data. @ -481,7 +481,7 @@ While the function has still been identified well, some of the noise is also exp \subsection{Multimodal Data} \label{subsec:semi_bimodal} % \begin{figure*}[t] \begin{figure}[t]  \centering  \includestandalone{figures/semi_bimodal_joint}  \includestandalone{figures/semi_bimodal_attrib} @ -517,7 +517,7 @@ At $x = -10$ the inferred modes and assignment processes start reverting to thei   \subsection{Mixed Cart-Pole Systems} \label{subsec:cartpole} \begin{table*}[t] \begin{table}[t]  \centering  \caption{  \label{tab:cartpole} @ -553,7 +553,7 @@ At $x = -10$ the inferred modes and assignment processes start reverting to thei  10 & GPR Short & {\textemdash} & {\textemdash} & -5.24 \pm 0.04 & -5.14 \pm 0.04 & \bfseries 0.903 \pm 0.003 & \bfseries 0.792 \pm 0.003 \\  \bottomrule  \end{tabular} \end{table*} \end{table} Our third experiment is based on the cart-pole benchmark for reinforcement learning as described by~\textcite{barto_neuronlike_1983} and implemented in OpenAI Gym~\parencite{brockman_openai_2016}. In this benchmark, the objective is to apply forces to a cart moving on a frictionless track to keep a pole, which is attached to the cart via a joint, in an upright position. We consider the regression problem of predicting the change of the pole's angle given the current state of the cart and the action applied.