找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

Titlebook: Computing and Data Science; Third International Weijia Cao,Aydogan Ozcan,Bei Guan Conference proceedings 2021 Springer Nature Singapore Pt

[复制链接]
楼主: GUST
发表于 2025-3-28 16:00:01 | 显示全部楼层
Polyp Segmentation Using Fully Convolutional Neural Network with Dropout and CBAMimization techniques: convoluted block attention module and dropout. We conducted and evaluated experiments with performance metrics and concluded that convoluted block attention module and dropout have positive influence on the model, and our optimized model has advantage over some state-of-art models.
发表于 2025-3-28 21:39:29 | 显示全部楼层
发表于 2025-3-29 02:00:26 | 显示全部楼层
发表于 2025-3-29 06:22:09 | 显示全部楼层
Evaluation of Quantization Techniques for Deep Neural Networks Post-training Quantization and Quantization-aware training. In addition, we also compare the results of different methods on two representative networks in DNNs: Resnet and Mobilenet, and analyse some ablation study. Further more, the remaining questions and future direction are summarized to boost the development of quantization method.
发表于 2025-3-29 07:35:51 | 显示全部楼层
Evaluation of the Effectiveness of COVID-19 Prevention and Control Based on Modified SEIR Modeld by examinating Characteristic polynomial, and global stability is proved by constructing Lyapunov Function. In addition, the effect of the epidemic prevention measures are evaluated by numerical simulation. The research shows that the post exposure infection rate and quarantine rate are the most crucial parameters of this disease.
发表于 2025-3-29 11:52:12 | 显示全部楼层
发表于 2025-3-29 18:21:00 | 显示全部楼层
发表于 2025-3-29 20:18:12 | 显示全部楼层
发表于 2025-3-30 01:54:54 | 显示全部楼层
发表于 2025-3-30 07:48:16 | 显示全部楼层
https://doi.org/10.1007/978-1-4615-0693-5 Post-training Quantization and Quantization-aware training. In addition, we also compare the results of different methods on two representative networks in DNNs: Resnet and Mobilenet, and analyse some ablation study. Further more, the remaining questions and future direction are summarized to boost the development of quantization method.
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 吾爱论文网 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
QQ|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-8-24 23:48
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表