Summary of Learning Granger Causality From Instance-wise Self-attentive Hawkes Processes, by Dongxia Wu et al.
Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes
by Dongxia Wu, Tsuyoshi Idé, Aurélie Lozano, Georgios Kollias, Jiří Navrátil, Naoki Abe, Yi-An Ma, Rose Yu
First submitted to arxiv on: 6 Feb 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 Instance-wise Self-Attentive Hawkes Processes (ISAHP) framework is a deep learning model designed to learn Granger causality from asynchronous, interdependent, multi-type event sequences. Unlike existing methods that require strong assumptions or heuristically defined parameters, ISAHP can directly infer instance-level causal structures without these limitations. By leveraging the self-attention mechanism of transformers, ISAHP meets the requirements of Granger causality and outperforms classical models in discovering complex instance-level causal structures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to discover patterns in large amounts of data that come from different sources and are related to each other. The researchers created a new way to analyze these events, called ISAHP, which can find the relationships between individual events without needing specific assumptions or rules. This is useful because it allows us to make more accurate predictions and decisions based on this information. |
Keywords
* Artificial intelligence * Deep learning * Self attention
