本文属于自然语言处理领域,标题中提到的 mixture of experts (MOE) 是一种在深度学习模型中经常用到的一个技巧,即把整个任务分拆成并列或串联的小任务,然后用不同的 expert network 来训练每一个小任务再将它们最后合在一起。例如在计算机视觉中,我们会用一个 expert network 来做 human detection(检测哪儿有人)...
Sparse Mixture-of-Experts are Domain Generalizable Learners. ICLR'23 作者:Bo Li, Yifei Shen, Jingkang Yang, Yezhen Wang, Jiawei Ren, Tong Che, Jun Zhang, Ziwei Liu [Domain Generalization] [Transfer Learning] 这篇文章研究了领域泛化中学习器(特指深度神经网络)的结构设计,并指出了transformer中的稀...
TeoDepartment of MathematicsNational University of Singaporerachel.tsy@u.nus.eduTan M. NguyenDepartment of MathematicsNational University of Singaporetanmn@nus.edu.sgAbstractSparse Mixture of Experts (SMoE) has become the key to unlocking unparalleledscalability in deep learning. SMoE has the potential ...
on model inference (i.e., no gradient computation) and achieves greater sparsity while maintaining or even improving performance on downstream tasks. EEP can be used to reduce both the total number of experts (thus saving GPU memory) and the...
Sparse mixture of experts provides larger model capacity while requiring a constant computational overhead. It employs the routing mechanism to distribute input tokens to the best-matched experts according to their hidden representations. However, learning such a routing mechanism encourages token ...
北京邮电大学 计算机科学技术博士 Papers | MoE-LLaVA: Mixture of Experts for Large Vision-Language ModelsFor Large Vision-Language Models (LVLMs), scaling the model can effectively improve performance. However, expanding model parameters significantly increases the training and inferring costs, ...
From scratch implementation of a sparse mixture of experts language model inspired by Andrej Karpathy's makemore :) - AviSoori1x/makeMoE
Triton-based implementation of Sparse Mixture of Experts. - GitHub - shawntan/scattermoe: Triton-based implementation of Sparse Mixture of Experts.
Learning Sparse Mixture of Experts for Visual Question Answering There has been a rapid progress in the task of Visual Question Answering with improved model architectures. Unfortunately, these models are usually computationally intensive due to their sheer size which poses a serious challenge for ...