Summary of On the Effectiveness Of Discrete Representations in Sparse Mixture Of Experts, by Giang Do et al.
On the effectiveness of discrete representations in sparse mixture of experts
by Giang Do, Kha Pham, Hung Le, Truyen Tran
First submitted to arxiv on: 28 Nov 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 The paper proposes a new architecture called Vector-Quantized Mixture of Experts (VQMoE) as an alternative to traditional routers in Sparse Mixture of Experts (SMoE), which aims to scale up model capacity without increasing computational costs. The VQMoE assigns experts to input via indirection, using discrete representations learned through vector quantization. This approach is shown to overcome the challenges present in traditional routers and achieves a 28% improvement in robustness compared to other SMoE routing methods, while maintaining strong performance in fine-tuning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops an innovative solution for scaling up model capacity without increasing computational costs. The new architecture, VQMoE, replaces traditional routers with discrete representations learned through vector quantization. This allows the expert network to better handle input data and improve overall robustness by 28% compared to other approaches. |
Keywords
» Artificial intelligence » Fine tuning » Mixture of experts » Quantization
