ScatterMoE是一种优化的Sparse Mixture-of-Experts模型实现,通过ParallelLinear组件和特化核函数减少了内存占用和提高执行速度,且支持易于扩展的PyTorch标准张量表示,为大规模深度学习模型的高效训练和推理提...
Sparse Mixture-of-Experts are Domain Generalizable Learnersarxiv.org/abs/2206.04046 作者:Bo Li, Yifei Shen, Jingkang Yang, Yezhen Wang, Jiawei Ren, Tong Che, Jun Zhang, Ziwei Liu Affiliation:S-Lab, Nanyang Technological University; The Hong Kong University of Science and Technology; Mila-...
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...
Sparse-MLP(MoE)网络作为一种新颖的深度学习架构,在图像分类领域展现出了独特的优势和潜力。通过引入Mixture of Experts机制,Sparse-MLP(MoE)网络在保持高性能的同时降低了计算复杂度,为深度学习在边缘计算和移动设备等资源受限场景中的应用提供了新的可能。随着研究的深入和技术的不断发展,相信Sparse-MLP(MoE)网络将...
We further improve the basic CIGN model by proposing a sparse mixture of experts model for difficult to classify samples which may get routed to suboptimal branches. If a sample has a routing confidence higher than a specific threshold, the sample may be routed towards multiple child nodes. ...
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 ...
Sparse Mixture of Experts (MoE) models are gaining traction due to their ability to enhance accuracy without proportionally increasing computational demands. Traditionally, significant computational resources have been invested in training dense Large Language Models (LLMs) with a single MLP layer...
Go Wider Instead of Deeper 当然,从损失函数上来讲,其实更像 Scaling Vision with Sparse Mixture of Experts 这篇文章。损失函数形式非常相似。 本文细节 本文主要将 Mixer-MLP 中的最后几层空间和通道 MLP 进行了替换,替换成了 MoE 结构(包含空间和通道两种结构)。这样的设定有助于引入更多的参数,提升模型的能...
The rapid advancement of large language models (LLMs) has led to architectures with billions to trillions of parameters, posing significant deployment challenges due to their substantial demands on memory, processing power, and energy consumption. Spar...
Sparse-MLP通过将稀疏的Mixture-of-Experts(MoE)层引入到MLP-Mixer模型中,实现了一种更高效的计算架构。其核心思想在于利用条件计算技术,即只激活部分专家(即网络中的一部分模块)来处理每个输入样本,从而在保持模型性能的同时降低计算成本。 技术细节 Sparse-MLP通过替换MLP-Mixer模型中的部分密集MLP块为稀疏块来实现这...