We trained the two autoencoders and the decoder using data from 10 model-based metabolic cost timeseries from simulated perturbed walking experiments. After training, we extracted the autoencoders and ...

Gray swan events are extreme weather events that cause local devestation, like Hurricane Lee in 2023, shown here. Increasingly powerful AI models can make short-term weather forecasts with surprising ...

Predicting class probabilities and using argmax for final class Achieving this is challenging, especially compared to traditional forecasting approaches. Start by setting a fixed encoder ...

Compute Koopman Operator: Estimate the Koopman operator using regularized regression (using Tikhonov regularization or SVD-based approach) on the collected data. The Koopman operator linearly relates ...

Time series forecasting plays a vital role in crucial decision-making processes across various industries such as retail, finance, manufacturing, and healthcare. However, compared to domains like ...

Forecasting Spatio-Temporal processes has been attracting a great deal of interest within the research community. In this context, there is an increasing trend of proposing and improving methodologies ...

This paper proposes to model medical data records with heterogeneous data types and bursty missing data using sequential variational autoencoders (VAEs). In particular, we propose a new methodology, ...