Summary of Neuro-symbolic Temporal Point Processes, by Yang Yang et al.
Neuro-Symbolic Temporal Point Processes
by Yang Yang, Chao Yang, Boyang Li, Yinghao Fu, Shuang Li
First submitted to arxiv on: 6 Jun 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 The neural-symbolic rule induction framework introduced in this paper efficiently discovers compact sets of temporal logic rules to explain irregular events of interest. The approach combines the temporal point process model with a differentiable loss function, guiding the learning of explanatory logic rules and their weights end-to-end. Predicates and logic rules are represented as vector embeddings, with predicate embeddings fixed and rule embeddings trained via gradient descent. A sequential covering algorithm is used to make the rule learning process more efficient and flexible. The approach demonstrates notable efficiency and accuracy across synthetic and real datasets, surpassing state-of-the-art baselines by a wide margin. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to find rules that explain interesting events in time. It’s like trying to figure out why something unusual happened at a certain moment. The researchers developed a special way to learn these rules using computers and math. They tested it on some real-world data and found that it was very good at finding the right explanations. |
Keywords
» Artificial intelligence » Gradient descent » Loss function
