检索结果-南通市图书馆

第40届中国控制会议

作者： Jinchen Wen Jianning Chi Chengdong Wu Xiaosheng Yu College of Information Science and Engineering Northeastern University College of Robot Science and Engineering Northeastern University

With the continuous development of computer vision technology,deep learning technology has been well applied in human pose ***,the latest human pose estimation algorithms can not solve the problem of interdependence and occlusion between joints,so the key point detection in human pose estimation is still a challenging *** this paper,a method based on masked pre-training model and lightweight SHNet combined method,random occlusion is performed on the key points of the human body in the picture,and the pre-training model is used to predict them,so that the problems of mutual occlusion and interdependence can be *** show that the algorithm is effective in key point detection of MPII and COCO 2017 datasets.

关键词： Pose Estimation High-Resolution Representation pre-training model

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

A PERT-BiLSTM-Att model for Online Public Opinion Text Sentiment Analysis

引用

Intelligent Automation & Soft Computing 2023年第8期37卷 2387-2406页

作者： Mingyong Li Zheng Jiang Zongwei Zhao Longfei Ma College of Computer and Information Science Chongqing Normal UniversityChongqing401331China

As an essential category of public event management and control,sentiment analysis of online public opinion text plays a vital role in public opinion early warning,network rumor management,and netizens’person-ality portraits under massive public opinion *** traditional sentiment analysis model is not sensitive to the location information of words,it is difficult to solve the problem of polysemy,and the learning representation ability of long and short sentences is very different,which leads to the low accuracy of sentiment *** paper proposes a sentiment analysis model PERT-BiLSTM-Att for public opinion text based on the pre-training model of the disordered language model,bidirectional long-term and short-term memory network and attention *** model first uses the PERT model pre-trained from the lexical location information of a large amount of corpus to process the text data and obtain the dynamic feature representation of the *** the semantic features are input into BiLSTM to learn context sequence information and enhance the model’s ability to represent long ***,the attention mechanism is used to focus on the words that contribute more to the overall emotional tendency to make up for the lack of short text representation ability of the traditional model,and then the classification results are output through the fully connected *** experimental results show that the classification accuracy of the model on NLPCC14 and weibo_senti_100k public data sets reach 88.56%and 97.05%,respectively,and the accuracy reaches 95.95%on the data set MDC22 composed of Meituan,Dianping and Ctrip *** proves that the model has a good effect on sentiment analysis of online public opinion texts on different *** experimental results on different datasets verify the model’s effectiveness in applying sentiment analysis of *** the same time,the model has a strong generalization ability and can achieve good results

关键词： Natural language processing PERT pre-training model emotional analysis BiLSTM

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Improving BERT Fine-Tuning via Self-Ensemble and Self-Distillation

引用

Journal of Computer Science & Technology 2023年第4期38卷 853-866页

作者：许一格邱锡鹏周浬皋黄萱菁 School of Computer Science Fudan UniversityShanghai 200433China Shanghai Key Laboratory of Intelligent Information Processing Fudan UniversityShanghai 200433China Huawei Technologies Co. Ltd.Hangzhou 310052China

Fine-tuning pre-trained language models like BERT have become an effective way in natural language processing(NLP)and yield state-of-the-art results on many downstream *** studies on adapting BERT to new tasks mainly focus on modifying the model structure,re-designing the pre-training tasks,and leveraging external data and *** fine-tuning strategy itself has yet to be fully *** this paper,we improve the fine-tuning of BERT with two effective mechanisms:self-ensemble and *** self-ensemble mechanism utilizes the checkpoints from an experience pool to integrate the teacher *** order to transfer knowledge from the teacher model to the student model efficiently,we further use knowledge distillation,which is called self-distillation because the distillation comes from the model itself through the time *** on the GLUE benchmark and the Text Classification benchmark show that our proposed approach can significantly improve the adaption of BERT without any external data or *** conduct exhaustive experiments to investigate the efficiency of the self-ensemble and self-distillation mechanisms,and our proposed approach achieves a new state-of-the-art result on the SNLI dataset.

关键词： BERT deep learning fine-tuning natural language processing(NLP) pre-training model

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Offline pre-trained Multi-agent Decision Transformer

引用

Machine Intelligence Research 2023年第2期20卷 233-248页

作者： Linghui Meng Muning Wen Chenyang Le Xiyun Li Dengpeng Xing Weinan Zhang Ying Wen Haifeng Zhang Jun Wang Yaodong Yang Bo Xu Institute of Automation Chinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Shanghai Jiao Tong University Shanghai 200240China School of Future Technology University of Chinese Academy of SciencesBeijing100049China Institute for AI Peking UniversityBeijing 100871China Department of Computer Science University College LondonLondon WC1E 6BTUK

Offline reinforcement learning leverages previously collected offline datasets to learn optimal policies with no necessity to access the real *** a paradigm is also desirable for multi-agent reinforcement learning(MARL)tasks,given the combinatorially increased interactions among agents and with the ***,in MARL,the paradigm of offline pre-training with online fine-tuning has not been studied,nor even datasets or benchmarks for offline MARL research are *** this paper,we facilitate the research by providing large-scale datasets and using them to examine the usage of the decision transformer in the context of *** investigate the generalization of MARL offline pre-training in the following three aspects:1)between single agents and multiple agents,2)from offline pretraining to online fine tuning,and 3)to that of multiple downstream tasks with few-shot and zero-shot *** start by introducing the first offline MARL dataset with diverse quality levels based on the StarCraftII environment,and then propose the novel architecture of multi-agent decision transformer(MADT)for effective offline *** leverages the transformer′s modelling ability for sequence modelling and integrates it seamlessly with both offline and online MARL tasks.A significant benefit of MADT is that it learns generalizable policies that can transfer between different types of agents under different task *** the StarCraft II offline dataset,MADT outperforms the state-of-the-art offline reinforcement learning(RL)baselines,including BCQ and *** applied to online tasks,the pre-trained MADT significantly improves sample efficiency and enjoys strong performance in both few-short and zero-shot *** the best of our knowledge,this is the first work that studies and demonstrates the effectiveness of offline pre-trained models in terms of sample efficiency and generalizability enhancements for MARL.

关键词： pre-training model multi-agent reinforcement learning(MARL) decision making transformer offline reinforcement learning

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：