Publikationen

exploRNN: Understanding Recurrent Neural Networks through Visual Exploration

exploRNN: Understanding Recurrent Neural Networks through Visual Exploration

Alex Bäuerle Ulm University Raphael Störk Ulm University Timo Ropinski Ulm University

arXiv:2012.06326, 2020

Abstract

Due to the success of deep learning and its growing job market, students and researchers from many areas are getting interested in learning about deep learning technologies. Visualization has proven to be of great help during this learning process, while most current educational visualizations are targeted towards one specific architecture or use case. Unfortunately, recurrent neural networks (RNNs), which are capable of processing sequential data, are not covered yet, despite the fact that tasks on sequential data, such as text and function analysis, are at the forefront of deep learning research. Therefore, we propose exploRNN, the first interactively explorable, educational visualization for RNNs. exploRNN allows for interactive experimentation with RNNs, and provides in-depth information on their functionality and behavior during training. By defining educational objectives targeted towards understanding RNNs, and using these as guidelines throughout the visual design process, we have designed exploRNN to communicate the most important concepts of RNNs directly within a web browser. By means of exploRNN, we provide an overview of the training process of RNNs at a coarse level, while also allowing detailed inspection of the data-flow within LSTM cells. Within this paper, we motivate our design of exploRNN, detail its realization, and discuss the results of a user study investigating the benefits of exploRNN.

Bibtex

content_copy
@preprint{baeuerle2020understanding,
    title={exploRNN: Understanding Recurrent Neural Networks through Visual  Exploration},
    author={B{\"a}uerle, Alex and St{\"o}rk, Raphael and Ropinski, Timo},
    institution={},
    year={2020},
    journal={arxiv preprint arXiv:arXiv:2012.06326}
}