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检索条件"机构=CAS Key Lab of Network Data Science and Technology Institute of Computing Technology"
383 条 记 录,以下是161-170 订阅
Beyond Model Scale Limits: End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration
arXiv
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arXiv 2024年
作者: Wu, Zhiyuan Sun, Sheng Wang, Yuwei Liu, Min Xu, Ke Pan, Quyang Gao, Bo Wen, Tian The State Key Lab of Processers Institute of Computing Technology Chinese Academy of Sciences Beijing China The University of Chinese Academy of Sciences Beijing China The Zhongguancun Laboratory Beijing China The Department of Computer Science and Technology Tsinghua University Beijing China The School of Computer Science and Technology The Engineering Research Center of Network Management Technology for High-Speed Railway of Ministry of Education Beijing Jiaotong University Beijing China
The rise of End-Edge-Cloud Collaboration (EECC) offers a promising paradigm for Artificial Intelligence (AI) model training across end devices, edge servers, and cloud data centers, providing enhanced reliability and ... 详细信息
来源: 评论
SDGNN: Learning Node Representation for Signed Directed networks
arXiv
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arXiv 2021年
作者: Huang, Junjie Shen, Huawei Hou, Liang Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China
network embedding is aimed at mapping nodes in a network into low-dimensional vector representations. Graph Neural networks (GNNs) have received widespread attention and lead to state-of-the-art performance in learnin... 详细信息
来源: 评论
PREP: Pre-training with Temporal Elapse Inference for Popularity Prediction
arXiv
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arXiv 2021年
作者: Cao, Qi Shen, Huawei Liu, Yuanhao Gao, Jinhua Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences China Cas Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China
Predicting the popularity of online content is a fundamental problem in various applications. One practical challenge takes roots in the varying length of observation time or prediction horizon, i.e., a good model for... 详细信息
来源: 评论
Self-Supervised GANs with label Augmentation
arXiv
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arXiv 2021年
作者: Hou, Liang Shen, Huawei Cao, Qi Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China
Recently, transformation-based self-supervised learning has been applied to generative adversarial networks (GANs) to mitigate catastrophic forgetting in the discriminator by introducing a stationary learning environm... 详细信息
来源: 评论
The minority matters: a diversity-promoting collaborative metric learning algorithm  22
The minority matters: a diversity-promoting collaborative me...
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Proceedings of the 36th International Conference on Neural Information Processing Systems
作者: Shilong Bao Qianqian Xu Zhiyong Yang Yuan He Xiaochun Cao Qingming Huang State Key Laboratory of Information Security Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Computer Science and Tech. University of Chinese Academy of Sciences Alibaba Group School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Laboratory of Big Data Mining and Knowledge Management CAS and Peng Cheng Laboratory
Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Following the convention of RS, existin...
来源: 评论
Match-ignition: Plugging PageRank into transformer for long-form text matching
arXiv
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arXiv 2021年
作者: Pang, Liang Lan, Yanyan Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China Institute for AI Industry Research Tsinghua University Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China
Neural text matching models have been widely used in community question answering, information retrieval, and dialogue. However, these models designed for short texts cannot well address the long-form text matching pr...
来源: 评论
Details Enhancement in Unsigned Distance Field Learning for High-fidelity 3D Surface Reconstruction  39
Details Enhancement in Unsigned Distance Field Learning for ...
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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025
作者: Xu, Cheng Hou, Fei Wang, Wencheng Qin, Hong Zhang, Zhebin He, Ying Key Laboratory of System Software (CAS) State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China School of Advanced Interdisciplinary Sciences University of Chinese Academy of Sciences China Department of Computer Science Stony Brook University United States InnoPeak Technology United States College of Computing and Data Science Nanyang Technological University Singapore
While Signed Distance Fields (SDF) are well-established for modeling watertight surfaces, Unsigned Distance Fields (UDF) broaden the scope to include open surfaces and models with complex inner structures. Despite the... 详细信息
来源: 评论
Deepfake network Architecture Attribution
arXiv
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arXiv 2022年
作者: Yang, Tianyun Huang, Ziyao Cao, Juan Li, Lei Li, Xirong Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China China
With the rapid progress of generation technology, it has become necessary to attribute the origin of fake images. Existing works on fake image attribution perform multi-class classification on several Generative Adver... 详细信息
来源: 评论
Transductive learning for unsupervised text style transfer
arXiv
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arXiv 2021年
作者: Xiao, Fei Pang, Liang Lan, Yanyan Wang, Yan Shen, Huawei Cheng, Xueqi Data Intelligence System Research Center Cas Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Institute for Ai Industry Research Tsinghua University University of Chinese Academy of Sciences Tencent Ai Lab
Unsupervised style transfer models are mainly based on an inductive learning approach, which represents the style as embeddings, decoder parameters, or discriminator parameters and directly applies these general rules... 详细信息
来源: 评论
BiKT: Unleashing the potential of GNNs via Bi-directional Knowledge Transfer
arXiv
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arXiv 2023年
作者: Zheng, Shuai Liu, Zhizhe Zhu, Zhenfeng Zhang, Xingxing Li, Jianxin Zhao, Yao The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Qiyuan Lab Beijing China The Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing100083 China
Based on the message-passing paradigm, there has been an amount of research proposing diverse and impressive feature propagation mechanisms to improve the performance of GNNs. However, less focus has been put on featu... 详细信息
来源: 评论