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This paper presents an extension of the regularized neural network architecture, called the LO-net. LO-net is a neural network architecture that can infer both the existence and values of hidden variables in streaming multivariate time series. The core idea is to initially make predictions with one network (the observable or O-net) based on a time delay embedding, following this with a gradual reduction...
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