LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks
This tool addresses the need for interpretability in black-box RNN models for researchers and practitioners in machine learning and related domains, though it is incremental as it builds on existing visualization and analysis techniques.
The researchers tackled the problem of understanding hidden state dynamics in recurrent neural networks, particularly LSTMs, by developing LSTMVis, a visual analysis tool that enables users to select input ranges, match state changes to patterns in data, and align results with domain annotations, as demonstrated in use cases on datasets with nesting, phrase structure, and chord progressions.
Recurrent neural networks, and in particular long short-term memory (LSTM) networks, are a remarkably effective tool for sequence modeling that learn a dense black-box hidden representation of their sequential input. Researchers interested in better understanding these models have studied the changes in hidden state representations over time and noticed some interpretable patterns but also significant noise. In this work, we present LSTMVIS, a visual analysis tool for recurrent neural networks with a focus on understanding these hidden state dynamics. The tool allows users to select a hypothesis input range to focus on local state changes, to match these states changes to similar patterns in a large data set, and to align these results with structural annotations from their domain. We show several use cases of the tool for analyzing specific hidden state properties on dataset containing nesting, phrase structure, and chord progressions, and demonstrate how the tool can be used to isolate patterns for further statistical analysis. We characterize the domain, the different stakeholders, and their goals and tasks.