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Lecture Notes in Computer Science
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 paralleldoi:10.1007/978-3-030-46147-8_28 fatcat:el3yecoypbcydbhmvqeshmnpk4