Summary of Combining Induction and Transduction For Abstract Reasoning, by Wen-ding Li et al.
Combining Induction and Transduction for Abstract Reasoning
by Wen-Ding Li, Keya Hu, Carter Larsen, Yuqing Wu, Simon Alford, Caleb Woo, Spencer M. Dunn, Hao Tang, Michelangelo Naim, Dat Nguyen, Wei-Long Zheng, Zenna Tavares, Yewen Pu, Kevin Ellis
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 A novel study in machine learning compares two approaches to inducing input-output mappings from few examples: inferring a latent function that explains the examples or directly predicting new test outputs using a neural network. The research focuses on the ARC benchmark, training neural models for induction and transduction on synthetically generated variations of Python programs solving ARC tasks. Results show that inductive and transductive models excel at different types of problems, with inductive synthesis performing well on precise computations and compositional concepts, while transduction succeeds on fuzzier perceptual concepts. Ensembling these models approaches human-level performance on the ARC benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new study in machine learning compares two ways to learn from a few examples: one way is to figure out how things work behind the scenes, and the other way is to just predict what will happen next. The researchers used a special test called ARC to compare these approaches. They found that each approach works better for different types of problems. When it comes to precise calculations and putting multiple ideas together, one approach does better. But when it’s about recognizing patterns and fuzzy concepts, the other approach shines. By combining both approaches, they were able to get really close to human-level performance on this test. |
Keywords
» Artificial intelligence » Machine learning » Neural network
