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检索条件"机构=Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education"
1095 条 记 录,以下是201-210 订阅
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Formal Reachability Analysis for Multi-Agent Reinforcement Learning Systems
Formal Reachability Analysis for Multi-Agent Reinforcement L...
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作者: Wang, Xiaoyan Peng, Jun Li, Shuqiu Li, Bing College of Computer Science and Technology Jilin University Changchun130012 China Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China
Reachability analysis is one of the most basic and challenging problems in verification. We investigate this problem in multi-agent reinforcement learning (MARL) system by transforming the reachability analysis to dec... 详细信息
来源: 评论
Closed Loop Networks for Open-Set Semi-Supervised Learning
SSRN
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SSRN 2023年
作者: Ouyang, Jihong Meng, Qingyi Li, Ximing Zhang, Zhengjie Li, Changchun Wang, Wenting College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University China Department of Computer Science University of Texas Dallas United States
Open-Set Semi-Supervised Learning (OS-SSL) refers to the task of learning classifiers with labeled and unlabeled instances, but the unlabeled data may contain the instances associated with unseen labels, dubbed as Out... 详细信息
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Todo: Task Offloading Decision Optimizer for the Efficient Provision of Offloading Schemes
SSRN
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SSRN 2023年
作者: Chen, Shilin Wang, Xingwang Sun, Yafeng College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computer Science and Technology Jilin University Changchun130012 China
As the volume of data stored on local devices increases, users turn to edge devices to help with processing tasks. Developing offloading schemes is challenging due to the varying configurations of edge devices and use... 详细信息
来源: 评论
Task-Oriented Multi-Modal Mutual Learning for Vision-Language Models
Task-Oriented Multi-Modal Mutual Learning for Vision-Languag...
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International Conference on Computer Vision (ICCV)
作者: Sifan Long Zhen Zhao Junkun Yuan Zichang Tan Jiangjiang Liu Luping Zhou Shengsheng Wang Jingdong Wang College of Computer Science and Technology Jilin University Jilin China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin China Baidu VIS University of Sydney Zhejiang University
Prompt learning has become one of the most efficient paradigms for adapting large pre-trained vision-language models to downstream tasks. Current state-of-the-art methods, like CoOp and ProDA, tend to adopt soft promp...
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IoT Device Identification via A Bio-Inspired Feature Selection Approach
IoT Device Identification via A Bio-Inspired Feature Selecti...
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IEEE International Conference on Communications (ICC)
作者: Boxiong Wang Hui Kang Geng Sun Jiahui Li College of Software Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China
The rapid development of the Internet-of-Things (IoT) also brings security and other problems. Device identification is a crucial tool for IoT security issues, which can detect and prevent cyber-attacks. Feature selec...
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Boosting few-shot point cloud segmentation with intra-class correlation and iterative prototype fusion
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Computer Vision and Image Understanding 2025年 258卷
作者: Zhang, Xindan Li, Ying Zhang, Xinnian College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China of Information and Communication Technology Ajou University Korea Republic of
Semantic segmentation of 3D point clouds is often limited by the challenge of obtaining labeled data. Few-shot point cloud segmentation methods, which can learn previously unseen categories, help reduce reliance on la... 详细信息
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A Description Model of Multi-Step Attack Planning Domain Based on knowledge Representation
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Chinese Journal of Electronics 2023年 第3期22卷 437-441页
作者: HU Liang XIE Nannan CHAI Sheng Nurbol Key Laboratory of Symbolic Computing and Knowledge Engineering of Ministry of Education Computer Science and Technology College Jilin University Changchun China Information Science and Engineering College Xinjiang University Urumqi China
Alerts of intrusion detection system are numerous, complex, and difficult to analyze. Alert correlation of multi-step attack is one of the solutions to this problem. Intelligence planning is an important research area... 详细信息
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Independent and Collaborative Demosaicking Neural Networks  5
Independent and Collaborative Demosaicking Neural Networks
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5th ACM International Conference on Multimedia in Asia, MMAsia 2023
作者: Niu, Yan Zhang, Lixue Li, Chenlai State Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Jilin University Jilin Changchun China College of Computer Science and Technology Jilin University Changchun China School of Artificial Intelligence Shenzhen Polytechnic University Shenzhen China
Existing demosaicking neural models generally reconstruct the red, green and blue channels by one unified network. This has the advantage of allowing the RGB channels to share latent features. However, it is unnoticed... 详细信息
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Semi-supervised multi-label learning with balanced binary angular margin loss  24
Semi-supervised multi-label learning with balanced binary an...
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Proceedings of the 38th International Conference on Neural Information Processing Systems
作者: Ximing Li Silong Liang Changchun Li Pengfei Wang Fangming Gu College of Computer Science and Technology Jilin University China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Computer Network Information Center Chinese Academy of Sciences China and University of Chinese Academy of Sciences Chinese Academy of Sciences China
Semi-supervised multi-label learning (SSMLL) refers to inducing classifiers using a small number of samples with multiple labels and many unlabeled samples. The prevalent solution of SSMLL involves forming pseudo-labe...
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Why Misinformation is Created? Detecting them by Integrating Intent Features
arXiv
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arXiv 2024年
作者: Wang, Bing Fu, Bo Li, Ximing Pei, Songwen Li, Changchun Wang, Shengsheng College of Computer Science and Technology Jilin University Changchun Jilin China Liaoning Normal University Liaoning Dalian China University of Shanghai for Science and Technology Shanghai China Key Laboratory of Symbolic Computation and Knowledge Engineering The Ministry of Education Jilin University China
Various social media platforms, e.g., Twitter and Reddit, allow people to disseminate a plethora of information more efficiently and conveniently. However, they are inevitably full of misinformation, causing damage to... 详细信息
来源: 评论