Summary of When Does Compositional Structure Yield Compositional Generalization? a Kernel Theory, by Samuel Lippl et al.
When does compositional structure yield compositional generalization? A kernel theory
by Samuel Lippl, Kim Stachenfeld
First submitted to arxiv on: 26 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neurons and Cognition (q-bio.NC)
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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 This paper presents a general theory of compositional generalization in kernel models with fixed representations. The authors find that these models are constrained by “conjunction-wise additivity,” which restricts their ability to learn certain tasks, such as transitively generalizing equivalence relations. Furthermore, the study identifies novel failure modes in compositional generalization caused by biases in training data, including memorization leak and shortcut bias. Empirical validation is provided through experiments with deep neural networks (convolutional networks, residual networks, and Vision Transformers) trained on compositional tasks. The findings have implications for understanding how statistical structure in training data affects compositional generalization and identifying remedies for failure modes in deep learning models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how machines learn to understand new combinations of things they’ve seen before. It’s called compositional generalization, and it’s important for making smart decisions. The researchers found that certain types of machine learning models can’t learn all tasks because of the way they’re structured. They also discovered some common mistakes these models make when trying to learn new things. By testing different kinds of neural networks on similar problems, the authors confirmed their findings and showed how statistical patterns in training data can affect a model’s ability to generalize. |
Keywords
» Artificial intelligence » Deep learning » Generalization » Machine learning
