BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Jason Eisner (Johns Hopkins University) DTSTART:20200820T190000Z DTEND:20200820T203000Z DTSTAMP:20260423T003301Z UID:IASML/21 DESCRIPTION:Title: E vent Sequence Modeling with the Neural Hawkes Process\nby Jason Eisner (Johns Hopkins University) as part of IAS Seminar Series on Theoretical M achine Learning\n\n\nAbstract\nSuppose you are monitoring discrete events in real time. Can you predict what events will happen in the future\, and when? Can you fill in past events that you may have missed? A probabili ty model that supports such reasoning is the neural Hawkes process (NHP)\, in which the Poisson intensities of K event types at time t depend on the history of past events. This autoregressive architecture can capture com plex dependencies. It resembles an LSTM language model over K word types\ , but allows the LSTM state to evolve in continuous time. \n\nThis talk w ill present the NHP model along with methods for estimating parameters (ML E 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 deductiv e database for a real-world domain\, which can track how possible event ty pes and other facts change over time. We take the system state to be a co llection of vector-space embeddings of these facts\, and derive a deep rec urrent architecture from the temporal Datalog program that specifies the d atabase. We call this method "neural Datalog through time."\n\nThis work was done with Hongyuan Mei and other collaborators including Guanghui Qin\ , Minjie Xu\, and Tom Wan.\n LOCATION:https://researchseminars.org/talk/IASML/21/ END:VEVENT END:VCALENDAR