This range is then used to match similar hidden state patterns displayed in the Match View (b). The selection is made by specifying a start-stop range in the text (c) and an activation threshold (t) which leads to a selection of hidden states (blue lines). The start-stop range can be further constrained using the pattern plot (d). The meta-tracks below depict extra information per word position like POS (e1) or the top K predictions (e2). The tool can then match this selection with similar hidden state patterns in the data set of varying lengths (f), providing insight into the representations learned by the model. The match view additionally includes user-defined meta-data encoded as heatmaps (g1,g2). The color of one heatmap (g2) can be mapped (h) to the word matrix (f) which allows the user to see patterns that lead to further refinement of the selection hypothesis. Navigation aids provide convenience (i1, i2). Abstract-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. Long-term usage data after putting the tool online revealed great interest in the machine learning community.
Similar to the previous work, LSTMVis REF designed an interactive visualization approach to visualize the hidden state representations of recurrent neural network and allows user to examine the internal behavior of LSTM model on different application scenarios.
LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
Computer Science Medicine