Before revision:  Next , we introduce the Brownian interval. This is a newand computationally efficientway of exactly sampling and reconstructing Brownian motion; this is in contrast to previous reconstruction techniques that are both approximate and relatively slow. This gives \textit{ \textit{  up to a 10.6\times speed improvement over previous techniques . After that , when specifically training Neural SDEs as GANs (Kidger et al. 2021), we demonstrate how SDE-GANs may be trained through careful weight clipping and choice of activation function.
After revision:  Next , we introduce the \textit{ \textit{ up to a 10.6\times speed improvement over previous techniques . After that , when specifically training Neural SDEs as GANs (Kidger et al. 2021), we demonstrate how SDE-GANs may be trained through careful weight clipping and choice of activation function.