Summary of Towards Generative Abstract Reasoning: Completing Raven’s Progressive Matrix Via Rule Abstraction and Selection, by Fan Shi et al.
Towards Generative Abstract Reasoning: Completing Raven’s Progressive Matrix via Rule Abstraction and Selection
by Fan Shi, Bin Li, Xiangyang Xue
First submitted to arxiv on: 18 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 proposed deep latent variable model, called Rule AbstractIon and SElection (RAISE), aims to endow machines with abstract reasoning ability by analyzing underlying rules and selecting missing images from Raven’s Progressive Matrix (RPM) tests. RAISE encodes image attributes into latent concepts and abstract atomic rules that act on these concepts. This allows the model to generate answers by selecting one atomic rule for each latent concept, constituting the underlying rule of an RPM. The proposed approach outperforms existing solvers in most configurations of realistic RPM datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines become smarter by teaching them how to solve puzzles like Raven’s Progressive Matrix tests. These tests are used to see if a machine can understand rules and use them to figure out what’s missing from a picture. The researchers developed a new way for computers to learn about these rules, which lets it generate answers that are better than other machines at solving this type of puzzle. |
