This talk will present the NHP model along with methods for estimating parameters (MLE and NCE), sampling predictions of the future (thinning), and imputing missing events (particle smoothing). I'll then show how to scale the NHP or the LSTM language model to large K, beginning with a temporal deductive database for a real-world domain, which can track how possible event types and other facts change over time. We take the system state to be a collection of vector-space embeddings of these facts, and derive a deep recurrent architecture from the temporal Datalog program that specifies the database. We call this method "neural Datalog through time."
This work was done with Hongyuan Mei and other collaborators including Guanghui Qin, Minjie Xu, and Tom Wan.