Summary of Cotode: Continuous Trajectory Neural Ordinary Differential Equations For Modelling Event Sequences, by Ilya Kuleshov et al.
COTODE: COntinuous Trajectory neural Ordinary Differential Equations for modelling event sequences
by Ilya Kuleshov, Galina Boeva, Vladislav Zhuzhel, Evgenia Romanenkova, Evgeni Vorsin, Alexey Zaytsev
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper proposes a novel approach to modeling event sequences by adopting a Gaussian Process view of events as observations of an actor’s dynamics. The authors modify the popular Neural ODE model by integrating these obtained dynamics, resulting in continuous-trajectory modeling. This allows for uncertainty estimation and the development of a negative feedback mechanism, which improves performance up to 20% AUROC compared to similar architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to model event sequences by treating events as observations of an actor’s dynamics governed by a Gaussian Process. This approach allows for continuous-trajectory modeling and uncertainty estimation, leading to the development of a negative feedback mechanism. The authors demonstrate state-of-the-art performance with their novel model. |
