Andre Lim

Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series

Learning accurate predictive models of real-world dynamic phenomena (e.g., climate, biological) remains a challenging task. One key issue is that the data generated by both natural and artificial processes often comprise time series that are irregularly sampled and/or contain missing observations. I…