We present a method for learning latent stochastic differential equations (SDEs) from high dimensional time series data. Given high-dimensional generated lower unknown Itô process, the proposed learns mapping ambient to space, and underlying SDE coefficients, through self-supervised approach. Using framework of variational autoencoders, we consider conditional generative model data based on Eul...