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Generalization Error Analysis for Sparse Mixture-of-Experts: A Preliminary Study
March 27, 2024, 4:41 a.m. | Jinze Zhao, Peihao Wang, Zhangyang Wang
cs.LG updates on arXiv.org arxiv.org
Abstract: Mixture-of-Experts (MoE) represents an ensemble methodology that amalgamates predictions from several specialized sub-models (referred to as experts). This fusion is accomplished through a router mechanism, dynamically assigning weights to each expert's contribution based on the input data. Conventional MoE mechanisms select all available experts, incurring substantial computational costs. In contrast, Sparse Mixture-of-Experts (Sparse MoE) selectively engages only a limited number, or even just one expert, significantly reducing computation overhead while empirically preserving, and sometimes even …
abstract analysis arxiv cs.lg data ensemble error expert experts fusion methodology moe predictions study through type
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