Summary of Swing-by Dynamics in Concept Learning and Compositional Generalization, by Yongyi Yang et al.
Swing-by Dynamics in Concept Learning and Compositional Generalization
by Yongyi Yang, Core Francisco Park, Ekdeep Singh Lubana, Maya Okawa, Wei Hu, Hidenori Tanaka
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 proposes an abstraction of compositional generalization problem by introducing a structured identity mapping (SIM) task, where a model is trained to learn the identity mapping on a Gaussian mixture with structurally organized centroids. The authors mathematically analyze the learning dynamics of neural networks trained on this SIM task and show that it captures key empirical observations on compositional generalization with diffusion models. The paper also offers new insights, such as non-monotonic learning dynamics of test loss in early phases of training. A text-conditioned diffusion model is trained to validate the predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machines learn and understand simple ideas. Researchers created a special task called the Structured Identity Mapping (SIM) task to help them study this process. They found that when they used this task, it matched what happened in earlier studies on more complex generative models. The results show that the SIM task is a useful way to think about how machines learn new concepts. |
Keywords
» Artificial intelligence » Diffusion model » Generalization
