Summary of Baking Symmetry Into Gflownets, by George Ma et al.
Baking Symmetry into GFlowNets
by George Ma, Emmanuel Bengio, Yoshua Bengio, Dinghuai Zhang
First submitted to arxiv on: 8 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 GFlowNets have shown promise in generating diverse candidates with high rewards. These networks incrementally generate objects and aim to learn a policy that assigns probability to sampling objects based on rewards. However, current training pipelines for GFlowNets do not consider isomorphic actions, which result in symmetric or identical states. This lack of symmetry increases the number of samples required and can lead to inefficient or incorrect flow functions, resulting in decreased reward and diversity of generated objects. Our study aims to integrate symmetries into GFlowNets by identifying equivalent actions during generation. Experimental results using synthetic data show promising performance for our proposed approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GFlowNets help create lots of different things with high values. They work by adding pieces one by one and trying to figure out which parts are most valuable. The problem is that these networks don’t consider actions that make identical outcomes, which makes them take longer to train and creates less valuable results. Our goal is to improve GFlowNets by recognizing similar actions during the creation process. We tested this idea using fake data and found it works well. |
Keywords
» Artificial intelligence » Probability » Synthetic data
