Summary of Decoupled Marked Temporal Point Process Using Neural Ordinary Differential Equations, by Yujee Song et al.
Decoupled Marked Temporal Point Process using Neural Ordinary Differential Equations
by Yujee Song, Donghyun Lee, Rui Meng, Won Hwa Kim
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper proposes a Decoupled Marked Temporal Point Process (MTPP) framework that disentangles the characterization of a stochastic process into a set of evolving influences from different events. By employing Neural Ordinary Differential Equations (Neural ODEs), the approach learns flexible continuous dynamics of these influences, addressing multiple inference problems such as density estimation and survival rate computation. This framework is compared with state-of-the-art methods on real-life datasets, highlighting the significance of disentangling influences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand complex events like money transactions or social media activity by breaking them down into smaller pieces that show how individual events affect the overall pattern over time. The new approach uses special neural networks to learn these patterns and also answers important questions like what’s the likelihood of an event happening and how long it takes for something to occur. |
Keywords
» Artificial intelligence » Density estimation » Inference » Likelihood
