@inproceedings{liang-etal-2018-cnns,
    title = "{CNN}s for {NLP} in the Browser: Client-Side Deployment and Visualization Opportunities",
    author = "Liang, Yiyun  and
      Tu, Zhucheng  and
      Huang, Laetitia  and
      Lin, Jimmy",
    editor = "Liu, Yang  and
      Paek, Tim  and
      Patwardhan, Manasi",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/N18-5013/",
    doi = "10.18653/v1/N18-5013",
    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."
}