Summary of Learning Causation Event Conjunction Sequences, by Thomas E. Portegys
Learning causation event conjunction sequences
by Thomas E. Portegys
First submitted to arxiv on: 17 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 investigates novel approaches for learning causations in event sequences. The researchers focus on identifying complex causal relationships between events, considering both their order and any intervening non-causal events that may affect the outcome. To achieve this, they employ a range of machine learning models, including recurrent and non-recurrent artificial neural networks (ANNs) and a histogram-based algorithm. Notably, an attention recurrent ANN outperformed other ANNs in this task, while the histogram-based approach demonstrated superior performance compared to all ANNs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how events can be connected to form cause-and-effect relationships. The researchers developed new ways to learn these connections using special types of computer programs called artificial neural networks (ANNs). They tested different types of ANNs and found that one type, which pays attention to important details, worked best. Another approach, based on counting patterns in data, was even better than the best-performing ANN. |
Keywords
* Artificial intelligence * Attention * Machine learning
