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检索条件"机构=Key Laboratories of Data Engineering and Knowledge Engineering"
1117 条 记 录,以下是101-110 订阅
排序:
Face micro-expression recognition algorithm based on ResNet depth model
Face micro-expression recognition algorithm based on ResNet ...
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6th International Conference on Intelligent Computing and Signal Processing (ICSP)
作者: Liquan Wang Shu Zhan School of Computer and Information Engineering Hefei University of Technology Key Laboratory of Big Data Knowledge Engineering Ministry of Education Hefei China
Micro Expression (ME) is the subtle facial expressions that people show when they express their inner feelings. To address the problem that micro-expression recognition is difficult and less accurate due to the small ...
来源: 评论
Rethinking data-Free Quantization as a Zero-Sum Game
arXiv
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arXiv 2023年
作者: Qian, Biao Wang, Yang Hong, Richang Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology China
data-free quantization (DFQ) recovers the performance of quantized network (Q) without accessing the real data, but generates the fake sample via a generator (G) by learning from full-precision network (P) instead. Ho... 详细信息
来源: 评论
Adaptive data-Free Quantization
arXiv
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arXiv 2023年
作者: Qian, Biao Wang, Yang Hong, Richang Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology China
data-free quantization (DFQ) recovers the performance of quantized network (Q) without accessing the original data, but generates the fake sample via a generator (G) by learning from full-precision network (P), which,... 详细信息
来源: 评论
Superclass Learning with Representation Enhancement
Superclass Learning with Representation Enhancement
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Conference on Computer Vision and Pattern Recognition (CVPR)
作者: Zeyu Gan Suyun Zhao Jinlong Kang Liyuan Shang Hong Chen Cuiping Li Key Lab of Data Engineering and Knowledge Engineering of MOE Renmin University of China Beijing China Renmin University of China Beijing China
In many real scenarios, data are often divided into a handful of artificial super categories in terms of expert knowledge rather than the representations of images. Concretely, a superclass may contain massive and var...
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基于数据挖掘技术的税务风险检测方法评述
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engineering 2024年 第3期34卷 43-59页
作者: Qinghua Zheng Yiming Xu Huixiang Liu Bin Shi Jiaxiang Wang Bo Dong School of Computer Science and Technology Xi’an Jiaotong UniversityXi’an 710049China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong UniversityXi’an 710049China School of Distance Education Xi’an Jiaotong UniversityXi’an 710049China
Tax risk behavior causes serious loss of fiscal revenue,damages the country’s public infrastructure,and disturbs the market economic order of fair *** recent years,tax risk detection,driven by information technology ... 详细信息
来源: 评论
EdgeNN: Efficient Neural Network Inference for CPU-GPU Integrated Edge Devices
EdgeNN: Efficient Neural Network Inference for CPU-GPU Integ...
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International Conference on data engineering
作者: Chenyang Zhang Feng Zhang Kuangyu Chen Mingjun Chen Bingsheng He Xiaoyong Du Key Laboratory of Data Engineering and Knowledge Engineering (MOE) and School of Information Renmin University of China School of Computing National University of Singapore
With the development of the architectures and the growth of AIoT application requirements, data processing on edge has become popular. Neural network inference is widely employed for data analytics on edge devices. Th...
来源: 评论
Generative-Contrastive Graph Learning for Recommendation
arXiv
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arXiv 2023年
作者: Yang, Yonghui Wu, Zhengwei Wu, Le Zhang, Kun Hong, Richang Zhang, Zhiqiang Zhou, Jun Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology China Ant Group China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Institute of Artificial Intelligence Hefei Comprehensive National Science Center China
By treating users’ interactions as a user-item graph, graph learning models have been widely deployed in Collaborative Filtering (CF) based recommendation. Recently, researchers have introduced Graph Contrastive Lear... 详细信息
来源: 评论
An Actor-centric Causality Graph for Asynchronous Temporal Inference in Group Activity
An Actor-centric Causality Graph for Asynchronous Temporal I...
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Conference on Computer Vision and Pattern Recognition (CVPR)
作者: Zhao Xie Tian Gao Kewei Wu Jiao Chang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology
The causality relation modeling remains a challenging task for group activity recognition. The causality relations describe the influence on the centric actor (effect actor) from its correlative actors (cause actors)....
来源: 评论
WATuning:A Workload-Aware Tuning System with Attention-Based Deep Reinforcement Learning
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Journal of Computer Science & Technology 2021年 第4期36卷 741-761页
作者: Jia-Ke Ge Yan-Feng Chai Yun-Peng Chai Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China Beijing 100872China School of Information Renmin University of ChinaBeijing 100872China College of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030027China
Configuration tuning is essential to optimize the performance of systems(e.g.,databases,key-value stores).High performance usually indicates high throughput and low *** present,most of the tuning tasks of systems are ... 详细信息
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Adaptive data-Free Quantization
Adaptive Data-Free Quantization
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Conference on Computer Vision and Pattern Recognition (CVPR)
作者: Biao Qian Yang Wang Richang Hong Meng Wang Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology China
data-free quantization (DFQ) recovers the performance of quantized network (Q) without the original data, but generates the fake sample via a generator (G) by learning from full-precision network (P), which, however, ...
来源: 评论