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Âé·³¸÷λ°ïæ·ÒëÕâһС¶Î»°£¬·Ç³£¸Ðл´ó¼Ò£¡Ð»Ð»£¡£¡£¡ 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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