数据挖掘是从数据中发现有价值的知识的计算过程,是现代数据科学的核心。它在许多领域有着巨大的应用,包括科学、工程、医疗保健、商业和医学。这些字段中的典型数据集是大的、复杂的,而且通常是有噪声的。从这些数据集中提取知识需要使用复杂的、高性能的、有原则的分析技术和算法。这些技术反过来又需要在高性能计算基础设施上的实现,这些基础设施需要经过仔细的性能调优。强大的可视化技术和有效的用户界面对于使数据挖掘工具吸引来自不同学科的研究人员、分析师、数据科学家和应用程序开发人员以及利益相关者的可用性也至关重要。SDM确立了自己在数据挖掘领域的领先地位,并为解决这些问题的研究人员提供了一个在同行评审论坛上展示其工作的场所。SDM强调原则方法和坚实的数学基础,以其高质量和高影响力的技术论文而闻名,并提供强大的研讨会和教程程序(包括在会议注册中)。 官网地址:http://dblp.uni-trier.de/db/conf/sdm/

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社交网络和分子图等结构化的图形数据在现实世界中随处可见。设计先进的图结构数据表示学习算法,促进下游任务的完成,具有重要的研究意义。图神经网络(GNNs)将深度神经网络模型推广到图结构数据,为从节点级或图级有效学习图结构数据表示开辟了一条新途径。由于其强大的表示学习能力,GNN在从推荐、自然语言处理到医疗保健等各种应用中获得了实际意义。近年来,它已成为一个热门的研究课题,越来越受到机器学习和数据挖掘界的关注。本教程涵盖了相关和有趣的主题,包括使用GNNs在图结构数据上的表示学习、GNNs的鲁棒性、GNNs的可扩展性和基于GNNs的应用程序。

目录内容:

  • 引言 Introduction
  • 基础 Foundations
  • 模型 Models
  • 应用 Applications

https://cse.msu.edu/~wangy206/tutorials/sdm2021/

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最新论文

3D Convolutional Neural Networks (CNNs) have been widely adopted for airway segmentation. The performance of 3D CNNs is greatly influenced by the dataset while the public airway datasets are mainly clean CT scans with coarse annotation, thus difficult to be generalized to noisy CT scans (e.g. COVID-19 CT scans). In this work, we proposed a new dual-stream network to address the variability between the clean domain and noisy domain, which utilizes the clean CT scans and a small amount of labeled noisy CT scans for airway segmentation. We designed two different encoders to extract the transferable clean features and the unique noisy features separately, followed by two independent decoders. Further on, the transferable features are refined by the channel-wise feature recalibration and Signed Distance Map (SDM) regression. The feature recalibration module emphasizes critical features and the SDM pays more attention to the bronchi, which is beneficial to extracting the transferable topological features robust to the coarse labels. Extensive experimental results demonstrated the obvious improvement brought by our proposed method. Compared to other state-of-the-art transfer learning methods, our method accurately segmented more bronchi in the noisy CT scans.

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