Summary of Fusemoe: Mixture-of-experts Transformers For Fleximodal Fusion, by Xing Han et al.
FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion
by Xing Han, Huy Nguyen, Carl Harris, Nhat Ho, Suchi Saria
First submitted to arxiv on: 5 Feb 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 As machine learning models tackle complex multimodal data, they face challenges in handling missing elements and temporal irregularities. To overcome these limitations, we introduce “FuseMoE”, a mixture-of-experts framework with an innovative gating function that integrates diverse modalities and handles missing values. Our approach is designed to improve predictive performance by leveraging complex data effectively. We demonstrate the practical utility of FuseMoE in real-world tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models are getting better at handling different types of data, but they still struggle when there’s a lot of missing information or if the data isn’t collected at regular intervals. To solve this problem, we created a new way to mix together different types of data using something called “FuseMoE”. It helps machines learn from complex data and make better predictions. |
Keywords
* Artificial intelligence * Machine learning * Mixture of experts
