Understanding Recurrent Neural Architectures by Analyzing and Synthesizing Long Distance Dependencies in Benchmark Sequential Datasets

In order to build efficient deep recurrent neural architectures, it isessential to analyze the complexity of long distance dependencies(LDDs) of the dataset being modeled. In this context, in this pa-per, we present detailed analysis of the complexity and the degreeof LDDs (orLDD characteristics) exhibited by various sequentialbenchmark datasets... (read more)

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