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Interpreting Differentiable Latent States for Healthcare Time-series Data
Understanding the meaning of latent states is crucial for interpreting machine learning models, assuming they capture underlying patterns.
G-CMP: Graph-enhanced Contextual Matrix Profile for unsupervised anomaly detection in sensor-based remote health monitoring
The Contextual Matrix Profile (CMP) is a configurable 2-dimensional version of the Matrix Profile (MP) that uses the distance matrix of all subsequences of a time series to discover patterns and anomalies.
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