NeuroView-RNN: It's About Time

Cj Barberan, Sina Alemmohammad, Naiming Liu, Randall Balestriero, Richard Baraniuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationProceedings of 2022 5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022
PublisherAssociation for Computing Machinery
Number of pages15
ISBN (Electronic)9781450393522
StatePublished - Jun 21 2022
Event5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022 - Virtual, Online, Korea, Republic of
Duration: Jun 21 2022Jun 24 2022

Publication series

NameACM International Conference Proceeding Series


Conference5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022
Country/TerritoryKorea, Republic of
CityVirtual, Online


  • Recurrent neural networks
  • interpretability
  • time series

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software


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