Summary of Interpreting Equivariant Representations, by Andreas Abildtrup Hansen et al.
Interpreting Equivariant Representations
by Andreas Abildtrup Hansen, Anna Calissano, Aasa Feragen
First submitted to arxiv on: 23 Jan 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 This paper investigates the impact of using latent representations from invariant and equivariant neural networks on downstream tasks. The authors demonstrate that ignoring the inductive bias imposed by an equivariant model can lead to decreased performance, whereas accounting for it can improve results. To address this issue, they propose principles for choosing invariant projections of latent representations. The study includes two examples: a permutation-equivariant variational auto-encoder for molecule graph generation and a rotation-equivariant representation for image classification. In both cases, the analysis of invariant latent representations outperforms their equivariant counterparts. The paper highlights the importance of considering inductive biases when using latent representations from equivariant models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how to use special kinds of neural networks called invariant and equivariant models. These models are designed to work with certain types of data, like images or molecules. The authors found that if you don’t account for the way these models were trained, your results might not be as good. They showed that by using a technique called invariant projection, you can get better results. The study included two examples: one where they generated molecule graphs and another where they classified images. In both cases, the authors found that using invariant projections worked better than using the original models. |
Keywords
* Artificial intelligence * Encoder * Image classification
