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#4475. Visual analytics tool for the interpretation of hidden states in recurrent neural networks
| August 2026 | publication date |
| Proposal available till | 15-05-2025 |
| 4 total number of authors per manuscript | 0 $ |
| The title of the journal is available only for the authors who have already paid for |
| Journal’s subject area: |
| Visual Arts and Performing Arts; Computer Science (miscellaneous); Computer Graphics and Computer-Aided Design; Medicine (miscellaneous); Computer Vision and Pattern Recognition; Software; |
| place 1 | place 2 | place 3 | place 4 |
| Free | Free | Free | Free |
| 2510 $ | 1340 $ | 1170 $ | 1000 $ |
Contract №4475.1   | Contract №4475.2 | Contract №4475.3 | Contract №4475.4 |
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Abstract:
In this paper, we introduce a visual analytics approach aimed at helping machine learning experts analyze the hidden states of layers in recurrent neural networks. Our technique allows the user to interactively inspect how hidden states store and process information throughout the feeding of an input sequence into the network. Our visual analytics approach comprises several components: First, our input visualization shows the input sequence and how it relates to the output (using color coding). In addition, hidden states are visualized through a nonlinear projection into a 2-D visualization space using t-distributed stochastic neighbor embedding to understand the shape of the space of the hidden states. Trajectories are also employed to show the details of the evolution of the hidden state configurations. Finally, a time-multi-class heatmap matrix visualizes the evolution of the expected predictions for multi-class classifiers, and a histogram indicates the distances between the hidden states within the original space. To demonstrate the capability of our approach, we discuss two typical use cases for long short-term memory models applied to two widely used natural language processing datasets.
Keywords:
Classification; Hidden states; Interpretability; Long short-term memory; Machine learning; Natural language processing; Nonlinear projection; Recurrent neural networks; Visual analytics; Visualization
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2 place - free (for sale)
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4 place - free (for sale)
Abstract:
In this paper, we introduce a visual analytics approach aimed at helping machine learning experts analyze the hidden states of layers in recurrent neural networks. Our technique allows the user to interactively inspect how hidden states store and process information throughout the feeding of an input sequence into the network. Our visual analytics approach comprises several components: First, our input visualization shows the input sequence and how it relates to the output (using color coding). In addition, hidden states are visualized through a nonlinear projection into a 2-D visualization space using t-distributed stochastic neighbor embedding to understand the shape of the space of the hidden states. Trajectories are also employed to show the details of the evolution of the hidden state configurations. Finally, a time-multi-class heatmap matrix visualizes the evolution of the expected predictions for multi-class classifiers, and a histogram indicates the distances between the hidden states within the original space. To demonstrate the capability of our approach, we discuss two typical use cases for long short-term memory models applied to two widely used natural language processing datasets.
Keywords:
Classification; Hidden states; Interpretability; Long short-term memory; Machine learning; Natural language processing; Nonlinear projection; Recurrent neural networks; Visual analytics; Visualization
Contacts :
