1. [Publications](/publications)
2. Rethinking full connectivity in recurrent neural networks
 
 # Rethinking full connectivity in recurrent neural networks

  ![](/sites/default/files/styles/wide/public/publications/sparseRNN.png?itok=TrmgHDzh)

 ## Authors



[Matthijs Van keirsbilck](/person/matthijs-van-keirsbilck)

[Alex Keller](/person/alex-keller)

Xiaodong Yang (NVIDIA)

 

 

 ## Publication Date



Sunday, March 3, 2019

 

 ## Published in



[arxiv paper](https://arxiv.org/pdf/1905.12340.pdf)

 

 ## Research Area



[Algorithms and Numerical Methods](/research-area/algorithms)

[Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence)

 

 

 ## External Links



[GTC2019 talk](https://on-demand-gtc.gputechconf.com/gtcnew/sessionview.php?sessionName=s9389-structural+sparsity%3a+speeding+up+training+and+inference+of+neural+networks+by+linear+algorithms)