Summary of Mamba Hawkes Process, by Anningzhe Gao et al.
Mamba Hawkes Process
by Anningzhe Gao, Shan Dai, Yan Hu
First submitted to arxiv on: 7 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The proposed Mamba Hawkes Process (MHP) addresses challenges in modeling irregular event sequences by leveraging the Mamba state space architecture to capture long-range dependencies and dynamic interactions. The paper introduces a novel application of the Mamba architecture to Hawkes processes, enhancing predictive capabilities. The authors also propose the Mamba Hawkes Process Extension (MHP-E), which combines Mamba and Transformer models. Experimental results demonstrate the superior performance of both MHP and MHP-E in various datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to understand and predict event sequences that don’t follow a regular pattern. It’s like trying to make sense of when people post on social media or when certain events happen in finance. The current methods struggle to capture the complexity of these events, but this new approach uses something called Mamba to do better. The results show that it works really well and can be used for things like predicting when someone might tweet about a certain topic. |
Keywords
* Artificial intelligence * Transformer
