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Found 6 papers, 3 papers with code
Latent Temporal Flows for Multivariate Analysis of Wearables Data
Latent Temporal Flows simultaneously recovers a transformation of the observed sequences into lower-dimensional latent representations via deep autoencoder mappings, and estimates a temporally-conditioned probabilistic model via normalizing flows.
PhysioMTL: Personalizing Physiological Patterns using Optimal Transport Multi-Task Regression
The proposed method learns an individual-specific predictive model from heterogeneous observations, and enables estimation of an optimal transport map that yields a push forward operation onto the demographic features for each task.
LatTe Flows: Latent Temporal Flows for Multivariate Sequence Analysis
We introduce Latent Temporal Flows (\emph{LatTe-Flows}), a method for probabilistic multivariate time-series analysis tailored for high dimensional systems whose temporal dynamics are driven by variations in a lower-dimensional discriminative subspace.
Generalizing Variational Autoencoders with Hierarchical Empirical Bayes
Recent methods have mitigated this issue by deterministically moment-matching an aggregated posterior distribution to an aggregate prior.
Sparse Multi-Output Gaussian Processes for Medical Time Series Prediction
In the scenario of real-time monitoring of hospital patients, high-quality inference of patients' health status using all information available from clinical covariates and lab tests is essential to enable successful medical interventions and improve patient outcomes.
Adaptive Randomized Dimension Reduction on Massive Data
In this paper we develop an approach for dimension reduction that exploits the assumption of low rank structure in high dimensional data to gain both computational and statistical advantages.
