Identifying Latent Stochastic Differential Equations
نویسندگان
چکیده
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 Euler-Maruyama approximation solutions. Furthermore, use recent results identifiability variable models show that can recover not only but also original variables, up an isometry, in limit infinite validate several simulated video processing tasks, where is known, real world datasets.
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ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2022
ISSN: ['1053-587X', '1941-0476']
DOI: https://doi.org/10.1109/tsp.2021.3131723