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Title: NeuroView-RNN: It’s About Time
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.  more » « less
Award ID(s):
1911094 1838177 1730574
NSF-PAR ID:
10371728
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency
Page Range / eLocation ID:
1683 to 1697
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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