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Events across a timeline are a common data representation, seen in differenttemporal modalities. Individual atomic events can occur in a certain temporalordering to compose higher level composite events. Examples of a compositeevent are a patient's medical symptom or a baseball player hitting a home run,caused distinct temporal orderings of patient vitals and player movementsrespectively. Such salient composite events are provided as labels in temporaldatasets and most works optimize models to predict these composite event labelsdirectly. We focus on uncovering the underlying atomic events and theirrelations that lead to the composite events within a noisy temporal datasetting. We propose Neural Temporal Logic Programming (Neural TLP) which firstlearns implicit temporal relations between atomic events and then lifts logicrules for composite events, given only the composite events labels forsupervision. This is done through efficiently searching through thecombinatorial space of all temporal logic rules in an end-to-end differentiablemanner. We evaluate our method on video and healthcare datasets where itoutperforms the baseline methods for rule discovery.

Learning Temporal Rules from Noisy Timeseries Data