Abstract
We propose FastPoint, a novel multivariate point process that enables fast and accurate learning and inference. FastPoint uses deep recurrent neural networks to capture complex temporal dependency patterns among different marks, while self-excitation dynamics within each mark are modeled with Hawkes processes. This results in substantially more efficient learning and scales to millions of correlated marks with superior predictive accuracy. Our construction also allows for efficient and parallel sequential Monte Carlo sampling for fast predictive inference. FastPoint outperforms baseline methods in prediction tasks on synthetic and real-world high-dimensional event data at a small fraction of the computational cost.
A. C. Türkmen—Work done while intern at Amazon.
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Notes
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The code is made available as part of MXNet. See https://github.com/apache/incubator-mxnet/blob/master/src/operator/contrib/hawkes_ll-inl.h.
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Türkmen, A.C., Wang, Y., Smola, A.J. (2020). FastPoint: Scalable Deep Point Processes. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Lecture Notes in Computer Science(), vol 11907. Springer, Cham. https://doi.org/10.1007/978-3-030-46147-8_28
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