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Found 3 papers, 2 papers with code
Time-to-Pattern: Information-Theoretic Unsupervised Learning for Scalable Time Series Summarization
Data summarization is the process of generating interpretable and representative subsets from a dataset.
A Survey of Techniques All Classifiers Can Learn from Deep Networks: Models, Optimizations, and Regularization
We then survey non-neural network learning algorithms that make innovative use of these methods to improve classification.
Activity2Vec: Learning ADL Embeddings from Sensor Data with a Sequence-to-Sequence Model
The widespread availability of sensors implanted in homes, smartphones, and smart watches have engendered collection of big datasets that reflect human behavior.
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