• Corpus ID: 248496391

ST-MoE: Designing Stable and Transferable Sparse Expert Models

@inproceedings{Zoph2022STMoEDS,
  title={ST-MoE: Designing Stable and Transferable Sparse Expert Models},
  author={Barret Zoph and Irwan Bello and Sameer Kumar and Nan Du and Yanping Huang and Jeff Dean and Noam M. Shazeer and William Fedus},
  year={2022}
}
Scale has opened new frontiers in natural language processing – but at a high cost. In response, Mixture-of-Experts (MoE) and Switch Transformers have been proposed as an energy efficient path to even larger and more capable language models. But advancing the state-of-the-art across a broad set of natural language tasks has been hindered by training instabilities and uncertain quality during fine-tuning. Our work focuses on these issues and acts as a design guide. We conclude by scaling a… 
Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts
TLDR
This work presents the Language-Image MoE, LIMoE, a sparse mixture of experts model capable of multimodal learning, and proposes an entropy-based regularization scheme for which it is demonstrated remarkable performance improvement over dense models of equivalent computational cost.

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