@InProceedings{liang-EtAl:2018:N18-5,
  author    = {Liang, Yiyun  and  Tu, Zhucheng  and  Huang, Laetitia  and  Lin, Jimmy},
  title     = {CNNs for NLP in the Browser: Client-Side Deployment and Visualization Opportunities},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {61--65},
  abstract  = {We demonstrate a JavaScript implementation of a convolutional neural network that performs feedforward inference completely in the browser. Such a deployment means that models can run completely on the client, on a wide range of devices, without making backend server requests. This design is useful for applications with stringent latency requirements or low connectivity. Our evaluations show the feasibility of JavaScript as a deployment target. Furthermore, an in-browser implementation enables seamless integration with the JavaScript ecosystem for information visualization, providing opportunities to visually inspect neural networks and better understand their inner workings.},
  url       = {http://www.aclweb.org/anthology/N18-5013}
}