Yao Ming, Shaozu Cao, Ruixiang Zhang, Zhen Li, Yuanzhe Chen, Yangqiu Song, and Huamin Qu
RNNVis is a visual analytics tool for understanding and comparing recurrent neural networks (RNNs) for text-based applications. The functions of hidden state units are explained using their expected response to the input texts (words). It allows users to gain more comprehensive understandings on the RNN's hidden mechanism through various visual techniques.
To appear in Proceedings of VAST 17. [preprint]
This project has used the following dataset: