Searching for Meaning in RNNs using Deep Neural Inspection
- Searching for Meaning in RNNs using Deep Neural Inspection
- Lin, Kevin
- Computer Science
- Persistent URL:
- Columbia University Computer Science Technical Reports
- Part Number:
- Department of Computer Science, Columbia University
- Publisher Location:
- New York
- Recent variants of Recurrent Neural Networks (RNNs)---in particular, Long Short-Term Memory (LSTM) networks---have established RNNs as a deep learning staple in modeling sequential data in a variety of machine learning tasks. However, RNNs are still often used as a black box with limited understanding of the hidden representation that they learn. Existing approaches such as visualization are limited by the manual effort to examine the visualizations and require considerable expertise, while neural attention models change, rather than interpret, the model. We propose a technique to search for neurons based on existing interpretable models, features, or programs.
- Neural networks (Computer science)
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- Suggested Citation:
- Kevin Lin, Eugene Wu, 2017, Searching for Meaning in RNNs using Deep Neural Inspection, Columbia University Academic Commons, https://doi.org/10.7916/D8H13DGD.