Temporal point processes (TPP) are probabilistic generative models for continuous-time
event sequences. Neural TPPs combine the fundamental ideas from point process literature …

Variability in social app usage across regions results in a high skew of the quantity and the
quality of check-in data collected, which in turn is a challenge for effective location …

Any human activity can be represented as a temporal sequence of actions performed to
achieve a certain goal. Unlike machine-made time series, these action sequences are highly …

Recent developments in predictive modeling using marked temporal point processes
(MTPPs) have enabled an accurate characterization of several real-world applications …

There exists a high variability in mobility data volumes across different regions, which
deteriorates the performance of spatial recommender systems that rely on region-specific …

A large fraction of data generated via human activities such as online purchases, health
records, spatial mobility, etc. can be represented as a sequence of events over a continuous …

The rapid growth of location-based services (LBS) has yielded massive amounts of data on
human mobility. Effectively extracting meaningful representations for user-generated check …

Continuous-time event sequences, ie, sequences consisting of continuous time stamps and
associated event types (“marks”), are an important type of sequential data with many …

Marked temporal point processes (MTPPs) are a general class of stochastic models for
modeling the evolution of events of different types (“marks”) in continuous time. These …

Marked temporal point processes (MTPPs) have emerged as a powerful modelingtool for a
wide variety of applications which are characterized using discreteevents localized in …