Summary of Learning More Generalized Experts by Merging Experts in Mixture-of-experts, By Sejik Park
Learning More Generalized Experts by Merging Experts in Mixture-of-Experts
by Sejik Park
First submitted to arxiv on: 19 May 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 This research paper investigates the challenges of deep learning in mixture-of-experts models, specifically the performance degradation caused by sharing layers. The authors propose an approach that tracks expert usage frequency and merges the most frequently selected experts to learn more general features. This algorithm enhances transfer learning and mitigates catastrophic forgetting in multi-domain task incremental learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning is like teaching a computer new skills! Researchers found that when they tried to teach different models (experts) together, it didn’t work as well as expected. They think this might be because the same thing is being learned multiple ways. To fix this, they came up with a clever way to merge the two best experts and use them to learn more general things. This helps computers remember what they’ve learned before and apply it to new situations. |
Keywords
» Artificial intelligence » Deep learning » Mixture of experts » Transfer learning
