图 书 馆开课单位--弗吉尼亚理工大学
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1Attention-based Graph Evolution[基于注意的图演化]
Shuangfei Fan(弗吉尼亚理工大学) Attention-based Graph Evolution
热度：29
Shuangfei Fan(弗吉尼亚理工大学) Attention-based Graph Evolution
热度：29
2Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks[异构网络中用于链路预测的上下文嵌入自监督学习]
Ping Wang(弗吉尼亚理工大学) Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks
热度：42
Ping Wang(弗吉尼亚理工大学) Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks
热度：42
3Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge Graphs[基于知识图逻辑查询的自监督双曲面表示]
Nurendra Choudhary(弗吉尼亚理工大学) Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge Graphs
热度：26
Nurendra Choudhary(弗吉尼亚理工大学) Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge Graphs
热度：26
4A First Look at DeepFake Videos in the Wild: Analysis and Detection[野外假视频的第一眼：分析和检测]
Jiameng Pu(弗吉尼亚理工大学) A First Look at DeepFake Videos in the Wild: Analysis and Detection
热度：34
Jiameng Pu(弗吉尼亚理工大学) A First Look at DeepFake Videos in the Wild: Analysis and Detection
热度：34
5Graph Structure Estimation Neural NetworksSelf-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks[异构网络中用于链路预测的上下文嵌入自监督学习]
Ping Wang(弗吉尼亚理工大学) Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks
热度：36
Ping Wang(弗吉尼亚理工大学) Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks
热度：36
6Benchmarking parameter estimation and reverse engineering strategies[标杆参数估计与逆向工程策略]
Pedro Mendes(弗吉尼亚理工大学) Parameter estimation has become a central problem in systems biology, both in the form of calibration of bottom-up models or as a component of reverse...
热度：70
Pedro Mendes(弗吉尼亚理工大学) Parameter estimation has become a central problem in systems biology, both in the form of calibration of bottom-up models or as a component of reverse...
热度：70
7Unifying Dependent Clustering and Disparate Clustering for Non-homogeneous Data[统一的依赖聚类和不同的非均匀数据聚类]
M. Shahriar Hossain(弗吉尼亚理工大学) 现代数据挖掘的设置涉及相结合的属性值的描述符在实体以及指定这些实体之间的关系。我们提出了一个集群这种非均匀数据集通过使用关系对依赖聚类或不同的聚类约...
热度：63
M. Shahriar Hossain(弗吉尼亚理工大学) 现代数据挖掘的设置涉及相结合的属性值的描述符在实体以及指定这些实体之间的关系。我们提出了一个集群这种非均匀数据集通过使用关系对依赖聚类或不同的聚类约...
热度：63
8Policy Informatics for Complex Systems[复杂系统的策略信息学]
Stephen Eubank(弗吉尼亚理工大学) Mental models are inadequate for coping with crises in complex socioeconomic systems. Modern information technology can support evidencebased policies...
热度：39
Stephen Eubank(弗吉尼亚理工大学) Mental models are inadequate for coping with crises in complex socioeconomic systems. Modern information technology can support evidencebased policies...
热度：39
9Extracting Temporal Signatures for Comprehending Systems Biology Models[用于理解系统生物学模型的时间特征提取]
Naren Sundaravaradan(弗吉尼亚理工大学 ) Systems biology has made massive strides in recent years, with capabilities to model complex systems including cell division, stress response, energy ...
热度：33
Naren Sundaravaradan(弗吉尼亚理工大学 ) Systems biology has made massive strides in recent years, with capabilities to model complex systems including cell division, stress response, energy ...
热度：33
10VACE Multimodal Meeting Corpus[VACE多式联运会议语料库]
Lei Chen(弗吉尼亚理工大学 ) In this paper, we report on the infrastructure we have de- veloped to support our research on multimodal cues for understanding meetings.With our focu...
热度：65
Lei Chen(弗吉尼亚理工大学 ) In this paper, we report on the infrastructure we have de- veloped to support our research on multimodal cues for understanding meetings.With our focu...
热度：65