Summary of Sigmoid Gating Is More Sample Efficient Than Softmax Gating in Mixture Of Experts, by Huy Nguyen et al.
Sigmoid Gating is More Sample Efficient than Softmax Gating in Mixture of Experts
by Huy Nguyen, Nhat Ho, Alessandro Rinaldo
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (stat.ML)
- 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 A novel mixture-of-experts (MoE) modeling study examines the properties of the sigmoid gating function, which has emerged as an alternative to the widely used softmax gating. The authors demonstrate that the sigmoid gating achieves superior performance in certain scenarios, but a theoretical foundation for this phenomenon is lacking. This paper addresses this gap by investigating the sample efficiency of the sigmoid gating in expert estimation tasks. The study employs a regression framework and shows that two distinct regimes arise, each with its own identifiability conditions and convergence rates. Notably, experts formulated using feed-forward networks with ReLU or GELU activation functions exhibit faster convergence rates under sigmoid gating compared to softmax gating. Moreover, the authors find that the sigmoid gating function requires fewer samples to attain the same error in expert estimation as its softmax counterpart. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new study looks at how a type of machine learning model called mixture-of-experts (MoE) works when using different “gating” functions. The most common one is called softmax, but researchers have been trying out another option called sigmoid. They wanted to know if the sigmoid gating function really does help the model work better, like some experiments suggested. To figure this out, they used a special kind of math problem called regression and showed that when you use the sigmoid gating function, your model can learn faster and make fewer mistakes than with softmax. This could be important for things like making predictions or recognizing patterns in data. |
Keywords
» Artificial intelligence » Machine learning » Mixture of experts » Regression » Relu » Sigmoid » Softmax
