Hidden Markov model interpretations of neural networks

Behavioral and Brain Sciences 23 (4):494-495 (2000)
Abstract
Page's manifesto makes a case for localist representations in neural networks, one of the advantages being ease of interpretation. However, even localist networks can be hard to interpret, especially when at some hidden layer of the network distributed representations are employed, as is often the case. Hidden Markov models can be used to provide useful interpretable representations.
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