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We describe a general methodology for the design of large-scale recursive neural network architectures (DAG-RNNs) which comprises three fundamental steps:
(1) representation of a given domain using suitable directed acyclic graphs (DAGs) to connect visible and hidden node variables;
(2) parameterization of the relationship between each variable and its parent variables by feedforward neural networks;
and (3) application of weight-sharing within appropriate subsets of DAG connections to capture stationarity and control model complexity.

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