
Neural Datalog Through Time: Informed Temporal Modeling via Logic Specification - benrbray
https://arxiv.org/abs/2006.16723
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benrbray
I've been reading about datalog + deductive databases recently, and stumbled
across this recent ICML 2020 paper! Had to shorten the original title, which
was 81 characters. The abstract:

> Learning how to predict future events from patterns of past events is
> difficult when the set of possible event types is large. Training an
> unrestricted neural model might overfit to spurious patterns.

> To exploit domain-specific knowledge of how past events might affect an
> event's present probability, we propose using a temporal deductive database
> to track structured facts over time. Rules serve to prove facts from other
> facts and from past events. Each fact has a time-varying state---a vector
> computed by a neural net whose topology is determined by the fact's
> provenance, including its experience of past events. The possible event
> types at any time are given by special facts, whose probabilities are
> neurally modeled alongside their states.

> In both synthetic and real-world domains, we show that neural probabilistic
> models derived from concise Datalog programs improve prediction by encoding
> appropriate domain knowledge in their architecture.

