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Fine-Grained $ε$-Margin Closed-Form Stabilization of Parametric Hawkes Processes
Hawkes Processes have undergone increasing popularity as default tools for modeling self- and mutually exciting interactions of discrete events in continuous-time event streams.
BAYESIAN COMPRESSED DEEP LEARNING FOR STATE ESTIMATION OF UNOBSERVABLE POWER SYSTEMS
In recent years, state-of-the-art Deep Learning (DL)-based modeling has been applied to the problem of state estimation of unobservable electrical distribution systems, with promising results.
Hawkes Processes Modeling, Inference and Control: An Overview
It has been used in a myriad of applications, ranging from finance and earthquakes to crime rates and social network activity analysis. Recently, a surge of different tools and algorithms have showed their way up to top-tier Machine Learning conferences.
Make Hawkes Processes Explainable by Decomposing Self-Triggering Kernels
We demonstrate that the new automatic kernel decomposition procedure outperforms the existing methods on the prediction of discrete events in real-world data.
