Summary of Is Temperature Sample Efficient For Softmax Gaussian Mixture Of Experts?, by Huy Nguyen et al.
Is Temperature Sample Efficient for Softmax Gaussian Mixture of Experts?
by Huy Nguyen, Pedram Akbarian, Nhat Ho
First submitted to arxiv on: 25 Jan 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 approach to mixture of experts (MoE) models is introduced, which combines dense and sparse gating mechanisms to control expert specialization during training. The dense-to-sparse gate helps stabilize expert weights by adjusting the temperature parameter in the softmax function. This paper investigates the effects of this gate on maximum likelihood estimation under Gaussian MoE models, showing that convergence rates can be slower than polynomial rates due to interactions between model parameters and temperature. To address this issue, a novel activation dense-to-sparse gate is proposed, which improves parameter estimation rates by imposing linearly independence conditions on activation functions. Theoretical results are validated through simulation studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MoE models have experts that learn from data and combine their outputs to make predictions. A new way of controlling these experts is explored, where a “dense-to-sparse” gate helps them work together better during training. This paper looks at how this gate affects the model’s ability to find the best weights for the experts. It shows that sometimes the model can take a long time to converge because of interactions between different parts of the model. To fix this, a new kind of gate is proposed that makes the model work faster and more accurately. |
Keywords
* Artificial intelligence * Likelihood * Mixture of experts * Softmax * Temperature
