Summary of Cumulative Hazard Function Based Efficient Multivariate Temporal Point Process Learning, by Bingqing Liu
Cumulative Hazard Function Based Efficient Multivariate Temporal Point Process Learning
by Bingqing Liu
First submitted to arxiv on: 21 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 paper proposes a novel approach to modeling temporal point processes by directly modeling the cumulative hazard function (CHF), which allows for accurate likelihood evaluation. The existing CHF-based methods lack mathematical constraints, leading to untrustworthy results. To address this issue, the authors develop a neural network-based model that learns a flexible but well-defined CHF and applies it to multivariate temporal point processes with low parameter complexity. Experimental results on six datasets demonstrate that the proposed model achieves state-of-the-art performance in data fitting and event prediction tasks while having significantly fewer parameters and memory usage than strong competitors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how events happen over time by creating a new way to model temporal point processes. Traditional methods have limitations, but this new approach uses neural networks to learn a flexible and well-defined model that can handle multiple types of events. The results show that this method is more accurate and efficient than others in predicting when events will happen. |
Keywords
» Artificial intelligence » Likelihood » Neural network
