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Chapter and Conference Paper
Robust Pedestrian Detection: Faster Deployments with Fusion of Models
Pedestrian detection has a wide range of real-world critical applications including security and management of emergency scenarios. In critical applications, detection recall and precision are both essential ...
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Chapter and Conference Paper
CARU: A Content-Adaptive Recurrent Unit for the Transition of Hidden State in NLP
This article introduces a novel RNN unit inspired by GRU, namely the Content-Adaptive Recurrent Unit (CARU). The design of CARU contains all the features of GRU but requires fewer training parameters. We make ...
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Chapter and Conference Paper
Variable-Depth Convolutional Neural Network for Text Classification
This article introduces a recurrent CNN based framework for the classification of arbitrary length text in natural sentence. In our model, we present a complete CNN design with recurrent structure to capture t...