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Found 5 papers, 3 papers with code
Representation Learning of Daily Movement Data Using Text Encoders
Time-series representation learning is a key area of research for remote healthcare monitoring applications.
Information Theory Inspired Pattern Analysis for Time-series Data
For applications with stochastic state transitions, features are developed based on Shannon's entropy of Markov chains, entropy rates of Markov chains, entropy production of Markov chains, and von Neumann entropy of Markov chains.
Loss Adapted Plasticity in Deep Neural Networks to Learn from Data with Unreliable Sources
When data is streaming from multiple sources, conventional training methods update model weights often assuming the same level of reliability for each source; that is: a model does not consider data quality of each source during training.
Designing A Clinically Applicable Deep Recurrent Model to Identify Neuropsychiatric Symptoms in People Living with Dementia Using In-Home Monitoring Data
Agitation is one of the neuropsychiatric symptoms with high prevalence in dementia which can negatively impact the Activities of Daily Living (ADL) and the independence of individuals.
When Complexity Is Good: Do We Need Recurrent Deep Learning For Time Series Outlier Detection?
Recurrent deep learning methods have a larger capacity for learning complex representations in time series data.
