Summary of Is Complexity An Illusion?, by Michael Timothy Bennett
Is Complexity an Illusion?
by Michael Timothy Bennett
First submitted to arxiv on: 31 Mar 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 paper investigates the relationship between simplicity and generalization in machine learning models. Researchers have long believed that simpler models are more likely to generalize well, identifying patterns with greater efficiency. However, this correlation may not always hold true, especially in interactive settings where interpretation plays a crucial role. The study’s theoretical work suggests that generalization is a consequence of “weak” constraints implied by function, rather than form. Experimental results demonstrate a significant improvement in generalization rates when choosing weak constraints over simple forms. This paper shows that all constraints can take equally simple forms, regardless of strength, and highlights the importance of considering spatially extended forms to force a correlation between simplicity and generalisation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores why simpler models tend to generalize well. It suggests that this is because simpler models have fewer parameters to adjust, making them more efficient at identifying patterns. However, the study also shows that not all simple models are created equal, and that some may be better at generalizing than others. The researchers found that when they used a combination of weak constraints and simple forms, their model was able to generalize much better than one that only used simple forms. |
Keywords
» Artificial intelligence » Generalization » Machine learning
