Visual analytics tool for the interpretation of hidden states in recurrent neural networks.

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. The technique can help answer questions, such as which parts of the input data have a higher impact on the prediction and how the model correlates each hidden state configuration with a certain output. 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

SUBMITTER: Garcia R 

PROVIDER: S-EPMC8479019 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature