在他们的工作中,作者选择了使用边级别 DP。 [24] Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith. Smooth sensitivity and sampling in private data analysis. In Proceedings of the thirty-ninth annual ACM symposium on Theory of computing, pages 75–84, 2007. 如上所述,与结构化数据库(如图像或...
网络释义 1. 图结构数据 ...蒋豪良 复旦大学 关键词:图结构数据 搜索 引言 图结构数据(Graph-structured Data), 是指表示为图的数据。www.docin.com|基于1 个网页 例句 释义: 全部,图结构数据 更多例句筛选 1. A data graph is a collection of tree-structured or graph-structured data objects. 数据图...
A central object in data analysis is a graph \\(G = (V,E)\\) defined by a set of vertices V and edges between those vertices E . The vertices can serve as a proxy for any data type (e.g., social network users, a company's products, and waypoints on a map), and the graph...
这篇文章研究的内容是:通过改变图的拓扑结构,来影响分类器模型的预测结果,进而研究分类器模型到底学到了什么,并且有效提升模型的鲁棒性。 这篇文章首先提出了一种基于强化学习的攻击方法,该方法只需要分类器模型的预测标签,就可以学习到有效的攻击策略;另外在给定模型梯度信息的情况下,提出了一种叫“梯度算法”的攻击...
LeCun. Deep convolutional net- works on graph-structured data. arXiv:1506.05163, 2015. 3M. Henaff, J. Bruna, and Y. LeCun, "Deep convolutional networks on graph-structured data," arXiv preprint arXiv:1506.05163, 2015.Henaff et al., 2015] Mikael Henaff, Joan Bruna, and Yann LeCun....
compactreachabilitylabelingforgraph-structureddata图结构数据的紧致可达标号 系统标签: reachabilitygraphlabelingstructured紧致compact CompactReachabilityLabelingforGraph-StructuredDataHaoHe†HaixunWang‡†DukeUniversityDurham,NC27708{haohe,junyang}@cs.duke.eduJunYang†PhilipS.Yu‡‡IBMT.J.WatsonResearchHawthor...
Quantum Walk Inspired Neural Networks for Graph-Structured DataIn recent years, along with the overwhelming advances in the field of neural information processing, quantum information processing (QIP) has shown significant progress in solving problems that are intractable on classical computers. Quantum ...
OpenGNN is a machine learning library for learning over graph-structured data. It was built with generality in mind and supports tasks such as:graph regression graph-to-sequence mappingIt supports various graph encoders including GGNNs, GCNs, SequenceGNNs and other variations of neural graph ...
Large amounts of graph-structured data are emerging from various avenues, ranging from natural and life sciences to social and semantic web communities. We address the problem of discovering subgraphs of entities that reflect latent topics in graph-structured data. These topics are structured meta-in...
Paper tables with annotated results for Graph Perceiver IO: A General Architecture for Graph Structured Data