COTODE: COntinuous Trajectory neural Ordinary Differential Equations for modelling event sequences
<|reference_start|>COTODE: COntinuous Trajectory neural Ordinary Differential Equations for modelling event sequences: Observation of the underlying actors that generate event sequences reveals that they often evolve continuously. Most modern methods, however, tend to model such processes through at most piecewise-continuous trajectories. To address this, we adopt a way of viewing events not as standalone phenomena but instead as observations of a Gaussian Process, which in turn governs the actor's dynamics. We propose integrating these obtained dynamics, resulting in a continuous-trajectory modification of the widely successful Neural ODE model. Through Gaussian Process theory, we were able to evaluate the uncertainty in an actor's representation, which arises from not observing them between events. This estimate led us to develop a novel, theoretically backed negative feedback mechanism. Empirical studies indicate that our model with Gaussian process interpolation and negative feedback achieves state-of-the-art performance, with improvements up to 20% AUROC against similar architectures.<|reference_end|>
@article{kuleshov2024cotode:,
    title={COTODE: COntinuous Trajectory neural Ordinary Differential Equations for
  modelling event sequences},
    author={Ilya Kuleshov, Galina Boeva, Vladislav Zhuzhel, Evgenia Romanenkova,
  Evgeni Vorsin, Alexey Zaytsev},
    journal={arXiv preprint arXiv:2408.08055},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08055},
    primaryClass={cs.LG cs.AI}
}