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检索条件"机构=MOE Key Laboratory of Symbolic Computation and Knowledge Engineering"
862 条 记 录,以下是181-190 订阅
排序:
ExplSched: Maximizing Deep Learning Cluster Efficiency for Exploratory Jobs
ExplSched: Maximizing Deep Learning Cluster Efficiency for E...
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IEEE International Conference on Cluster Computing
作者: Hongliang Li Hairui Zhao Zhewen Xu Xiang Li Haixiao Xu College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Changchun China High Performance Computing Center Jilin University China
Resource management for Deep Learning (DL) clusters is essential for system efficiency and model training quality. Existing schedulers provided by DL frameworks are mostly adaptations from traditional HPC clusters and...
来源: 评论
DISLOCATIONS WITH CORNERS IN AN ELASTIC BODY WITH APPLICATIONS TO FAULT DETECTION
arXiv
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arXiv 2023年
作者: Diao, Huaian Liu, Hongyu Meng, Qingle School of Mathematics Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Jilin Changchun China Department of Mathematics City University of Hong Kong Kowloon Tong Hong Kong
This paper focuses on an elastic dislocation problem that is motivated by applications in the geophysical and seismological communities. In our model, the displacement satisfies the Lamé system in a bounded domai... 详细信息
来源: 评论
Learning Group-Disentangled Representation for Interpretable Thoracic Pathologic Prediction
Learning Group-Disentangled Representation for Interpretable...
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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
作者: Li, Hao Wu, Yirui Hu, Hexuan Lu, Hu Lai, Yong Wan, Shaohua Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources China College of Computer and Information Hohai University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China School of Computer Science and Communication Engineering Jiangsu University China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China
Deep learning methods have shown significant performance in medical image analysis tasks. However, they generally act like 'black box' without explanations in both feature extraction and decision processes, le... 详细信息
来源: 评论
NegativePrompt: Leveraging Psychology for Large Language Models Enhancement via Negative Emotional Stimuli
arXiv
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arXiv 2024年
作者: Wang, Xu Li, Cheng Chang, Yi Wang, Jindong Wu, Yuan School of Artificial Intelligence Jilin University China Institute of Software CAS China Microsoft Research Asia China Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University China International Center of Future Science Jilin University China
Large Language Models (LLMs) have become integral to a wide spectrum of applications, ranging from traditional computing tasks to advanced artificial intelligence (AI) applications. This widespread adoption has spurre... 详细信息
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Scalable Precise computation of Shannon Entropy
arXiv
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arXiv 2025年
作者: Lai, Yong Tong, Haolong Xu, Zhenghang Yin, Minghao College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China School of Computer Science and Information Technology Northeast Normal University Changchun130017 China
Quantitative information flow analyses (QIF) are a class of techniques for measuring the amount of confidential information leaked by a program to its public outputs. Shannon entropy is an important method to quantify... 详细信息
来源: 评论
A Re-Parametrization-Based Bayesian Differential Analysis Algorithm for Gene Regulatory Networks Modeled with Structural Equation Models
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Computer Modeling in engineering & Sciences 2020年 第7期124卷 303-313页
作者: Yan Li Dayou Liu Yungang Zhu Jie Liu College of Computer Science and Technology Jilin UniversityChangchun130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun130012China
Under different conditions,gene regulatory networks(GRNs)of the same gene set could be similar but *** differential analysis of GRNs under different conditions is important for understanding condition-specific gene re... 详细信息
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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
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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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Learning with Partial Labels from Semi-supervised Perspective
arXiv
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arXiv 2022年
作者: Li, Ximing Jiang, Yuanzhi Li, Changchun Wang, Yiyuan Ouyang, Jihong College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of MOE Jilin University China College of Information Science and Technology Northeast Normal University China Key Laboratory of Applied Statistics of MOE Northeast Normal University China
Partial Label (PL) learning refers to the task of learning from the partially labeled data, where each training instance is ambiguously equipped with a set of candidate labels but only one is valid. Advances in the re... 详细信息
来源: 评论