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Contextual Unsupervised Outlier Detection in Sequences
This work proposes an unsupervised learning framework for trajectory (sequence) outlier detection that combines ranking tests with user sequence models.
Infinity Learning: Learning Markov Chains from Aggregate Steady-State Observations
We consider the task of learning a parametric Continuous Time Markov Chain (CTMC) sequence model without examples of sequences, where the training data consists entirely of aggregate steady-state statistics.
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