Summary of A Relational Inductive Bias For Dimensional Abstraction in Neural Networks, by Declan Campbell et al.
A Relational Inductive Bias for Dimensional Abstraction in Neural Networks
by Declan Campbell, Jonathan D. Cohen
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 explores how a “relational bottleneck” in neural networks can improve their ability to learn factorized representations and exhibit compositional coding, leading to increased generalization capabilities and learning efficiency. This mechanism focuses processing on relationships among inputs, allowing networks to develop orthogonal representations of feature dimensions and mimic human biases towards regularity without pre-specified symbolic primitives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The relational bottleneck helps neural networks learn like humans do, by focusing on connections between things rather than just individual pieces of information. This allows the network to develop a more flexible understanding of the world, making it better at learning and applying what it knows in new situations. |
Keywords
* Artificial intelligence * Generalization
