Summary of A Bayesian Mixture Model Of Temporal Point Processes with Determinantal Point Process Prior, by Yiwei Dong et al.
A Bayesian Mixture Model of Temporal Point Processes with Determinantal Point Process Prior
by Yiwei Dong, Shaoxin Ye, Yuwen Cao, Qiyu Han, Hongteng Xu, Hanfang Yang
First submitted to arxiv on: 7 Nov 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 paper proposes a novel approach to asynchronous event sequence clustering using Bayesian mixture models of Temporal Point Processes with Determinantal Point Process priors (TP2DP2). The proposed method aims to group similar event sequences in an unsupervised manner, while avoiding overfitting and excessive cluster generation. The authors introduce an efficient posterior inference algorithm based on conditional Gibbs sampling, enabling automatic identification of the potential number of clusters and accurate grouping of sequences with similar features. The TP2DP2 framework is applicable to a wide range of parametric temporal point processes, including neural network-based models. Experimental results on both synthetic and real-world data demonstrate the effectiveness of the proposed method in producing moderately fewer yet more diverse mixture components, achieving outstanding results across multiple evaluation metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us group similar event sequences without needing to tell it how many groups to make or what each group looks like. It uses a special kind of math called Bayesian mixture models to find patterns in the data and put similar things together. The method is flexible and can work with different types of data, including those that involve artificial intelligence. When tested on real-world data, the approach did well at finding meaningful groups and avoiding too many or too few groups. This could be useful in many areas where event sequences are important, such as analyzing human behavior or financial transactions. |
Keywords
» Artificial intelligence » Clustering » Inference » Neural network » Overfitting » Unsupervised
