Training DataResultBenchmark Image ClassificationImageNetV-MoE-L/16 (Every-2)Top 1 Accuracy87.41%# 87 Compare Number of params3400M# 1051 Compare Image ClassificationImageNetVIT-H/14Top 1 Accuracy88.08%# 60 Com
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, as all model parameters are activated for...
这就是 Sparse attention 类的论文的核心出发点，其中的关键就是用什么算法去压缩 token 数量，NSA 也...
Finally, we present the in- troduced mixture of experts feature compensator (MEFC). 3.1. Overall pipeline framework. Given a rainy image Irain ∈ RH×W ×3, where H × W represents the spatial resolution of the feature map, we perform overlapped image patch emb...
3️⃣面向稀疏的模型架构重构剧变案例：MosaicML团队将N-SA与MoE（Mixture of Experts）结合，设计...
Motivated by this we investigate the importance of leveraging "sparse" computation and propose SiRA: sparse mixture of low rank adaption. SiRA leverages the Sparse Mixture of Expert(SMoE) to boost the performance of LoRA. Specifically it enforces the top k experts routing with a capacity limit ...
While approaches like Mixture of Experts (MoE) decouple parameter count from computational complexity, they still face challenges in inference due to high memory access costs. This work introduces UltraMem, incorporating large-scale, ultra-sparse memory layer to address these limitations. Our approach ...
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 ...
In this work, we study the PEFT method for LLMs with the Mixture-of-Experts (MoE) architecture and the contents of this work are mainly threefold: (1) We investigate the dispersion degree of the activated experts in customized tasks, and found that the routing distribution for a specific ...
Large and sparse feed-forward layers (S-FFN) such as Mixture-of-Experts (MoE) have proven effective in scaling up Transformers model size for \textit{pretraining} large language models. By only activating part of the FFN parameters conditioning on input, S-FFN improves generalization performance...