Abstract
With the continuous development of deep learning, more and more huge deep learning models are developed by researchers, which leads to an exponential increase of the parameters of models. Therein, the convolutional recurrent network as a type of widely used deep learning method is often employed to handle spatiotemporal data, e.g., traffic data. However, because of the large number of parameters in the model, the convolutional recurrent network needs to consume a lot of computing resources and time in the training process. To reduce the consumption of resources, we propose a sparse convolutional recurrent network with a sparse gating mechanism that is able to reduce the complexity of the network by an improved gate unit while keeping the performance of the model. We evaluate the performance of our proposed network on traffic flow datasets, and the experimental results show that the parameters of the model are significantly reduced under the condition of similar prediction accuracy compared with the traditional convolutional recurrent network.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.