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egin{floatrow}[3]
ffigbox{ caption{Vectorisation comparisonfor batch learning (batch size 100, samples of 10 time steps)} label{fig:batchcmp}}{includegraphics[width=4.7cm,height=4.5cm]{picture//batchcomp_bold2}}ffigbox{ caption{Batch learning with Gradient Descent} label{fig:batch} }{includegraphics[width=4.7cm,height=4.5cm]{picture//batchsize_bold2}}ffigbox{caption{Comparison with Optimization Algorithms} label{fig:lbfgs}}
{includegraphics[width= 4.7cm,height=4.5cm]{picture//lbfgs2_bold}}end{floatrow}
end{figure}
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egin{figure*}vspace{-.2cm}
egin{centering}
subfigure[]{includegraphics[width=4.6cm,height=3.5cm]{picture//batchsize_bold2}label{fig:batch}}subfigure[]{includegraphics[width=4.6cm,height=3.5cm]{picture//batchcomp_bold2}label{fig:batchcmp}}subfigure[]{includegraphics[width= 4.6cm,height=3.5cm]{picture//lbfgs2_bold}label{fig:lbfgs}}end{centering}vspace{-.2cm}
caption{(a) batch learning with gradient descent; (b) vectorization comparisonfor (mini-)batch learning, where the batch size is 100 and samples of 10 time steps; and (c) comparison with optimization algorithms.}
end{figure*}