Interpretable Models for Understanding Immersive Simulations
IJCAI 2020, pp. 2319-2325, 2020.
Our results are important for any unsupervised machine learning task for which interpretability is an important criterion, because in such cases the model selection problem will be encountered
This paper describes methods for comparative evaluation of the interpretability of models of high dimensional time series data inferred by unsupervised machine learning algorithms. The time series data used in this investigation were logs from an immersive simulation like those commonly used in education and healthcare training. The struc...More
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