Neural Networks for irregularly observed continuous-time Stochastic Processes

ICLR 2018 Francois W. BellettiAlexander KuJoseph E. Gonzalez

Designing neural networks for continuous-time stochastic processes is challenging, especially when observations are made irregularly. In this article, we analyze neural networks from a frame theoretic perspective to identify the sufficient conditions that enable smoothly recoverable representations of signals in L^2(R)... (read more)

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