How Much Deep Is Deep Enough?

Diego Uribe, Enrique Cuan


Typical deep learning models defined in terms of multiple layers are based on the assumption that a better representation is obtained with a hierarchical model rather than with a shallow one. Nevertheless, increasing the depth of the model by increasing the number of layers can lead to the model being lost or stuck during the optimization process.This paper investigates the impact of linguistic complexity characteristics from text on a deep learning model defined in terms of a stacked architecture. As the optimal number of stacked recurrent neural layers is specific to each application, we examine the optimal number of stacked recurrent layers corresponding to each linguistic characteristic. Last but not least, we also analyze the computational cost demanded by increasing the depth of a stacked recurrent architecture implemented for a linguistic characteristic.


Recurrent neural networks, stacked architectures, linguistic characteristics

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