The increasing availability of large collections of electronic health record (EHR) data and
unprecedented technical advances in deep learning (DL) have sparked a surge of research …

Machine learning can be used to make sense of healthcare data. Probabilistic machine
learning models help provide a complete picture of observed data in healthcare. In this …

Recent progress in neural forecasting accelerated improvements in the performance of large-
scale forecasting systems. Yet, long-horizon forecasting remains a very difficult task. Two …

Multi-horizon forecasting often contains a complex mix of inputs–including static (ie time-
invariant) covariates, known future inputs, and other exogenous time series that are only …

Real-world time-series datasets are often multivariate with complex dynamics. To capture
this complexity, high capacity architectures like recurrent-or attention-based sequential deep …

Current approaches for multi-horizon time series forecasting using recurrent neural networks
(RNNs) focus on issuing point estimates, which is insufficient for decision-making in critical …

The COVID-19 global pandemic is a threat not only to the health of millions of individuals,
but also to the stability of infrastructure and economies around the world. The disease will …

The construction of an accurate train control model (TCM) is crucial to the design of
automatic train operation and real-time traffic management systems in high-speed railways …

The recent availability of electronic health records (EHRs) have provided enormous
opportunities to develop artificial intelligence (AI) algorithms. However, patient privacy has …

Understanding the predictions of a machine learning model can be as crucial as the model's
accuracy in many application domains. However, the black-box nature of most highly …