NeuroView-RNN: It's About Time

• URL: http://arxiv.org/abs/2202.11811v1
• Date: Wed, 23 Feb 2022 22:29:11 GMT
• Title: NeuroView-RNN: It's About Time
• Authors: CJ Barberan, Sina Alemohammad, Naiming Liu, Randall Balestriero, Richard G. Baraniuk
• Abstract summary: A key interpretability issue with RNNs is that it is not clear how each hidden state per time step contributes to the decision-making process. We propose NeuroView-RNN as a family of new RNN architectures that explains how all the time steps are used for the decision-making process. We showcase the benefits of NeuroView-RNN by evaluating on a multitude of diverse time-series datasets.
• Score: 25.668977252138905
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Recurrent Neural Networks (RNNs) are important tools for processing sequential data such as time-series or video. Interpretability is defined as the ability to be understood by a person and is different from explainability, which is the ability to be explained in a mathematical formulation. A key interpretability issue with RNNs is that it is not clear how each hidden state per time step contributes to the decision-making process in a quantitative manner. We propose NeuroView-RNN as a family of new RNN architectures that explains how all the time steps are used for the decision-making process. Each member of the family is derived from a standard RNN architecture by concatenation of the hidden steps into a global linear classifier. The global linear classifier has all the hidden states as the input, so the weights of the classifier have a linear mapping to the hidden states. Hence, from the weights, NeuroView-RNN can quantify how important each time step is to a particular decision. As a bonus, NeuroView-RNN also offers higher accuracy in many cases compared to the RNNs and their variants. We showcase the benefits of NeuroView-RNN by evaluating on a multitude of diverse time-series datasets.

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