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arXiv · 2012.06326

exploRNN: Understanding Recurrent Neural Networks through Visual Exploration

Alex Bäuerle·Patrick Albus·Raphael Störk·Tina Seufert·Timo Ropinski
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Abstract

Due to the success of deep learning (DL) and its growing job market, students and researchers from many areas are interested in learning about DL 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, recurrent neural networks (RNNs), which are capable of processing sequential data, are not covered yet. This is despite the fact that tasks on sequential data, such as text and function analysis, are at the forefront of DL research. Therefore, we propose exploRNN, the first interactively explorable educational visualization for RNNs. On the basis of making learning easier and more fun, we define educational objectives targeted towards understanding RNNs. We use these objectives to form guidelines for the visual design process. By means of exploRNN, which is accessible online, we provide an overview of the training process of RNNs at a coarse level, while also allowing a detailed inspection of the data flow within LSTM cells. In an empirical study, we assessed 37 subjects in a between-subjects design to investigate the learning outcomes and cognitive load of exploRNN compared to a classic text-based learning environment. While learners in the text group are ahead in superficial knowledge acquisition, exploRNN is particularly helpful for deeper understanding of the learning content. In addition, the complex content in exploRNN is perceived as significantly easier and causes less extraneous load than in the text group. The study shows that for difficult learning material such as recurrent networks, where deep understanding is important, interactive visualizations such as exploRNN can be helpful.

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BibTeXRIS

Alex Bäuerle, Patrick Albus, Raphael Störk, Tina Seufert, Timo Ropinski. 2020-12-09. exploRNN: Understanding Recurrent Neural Networks through Visual Exploration. https://doi.org/10.1007/s00371-022-02593-0

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