数据增强在机器学习领域多指采用一些方法(比如数据蒸馏,正负样本均衡等)来提高模型数据集的质量,增强数据。

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Code:https://github.com/Shen-Lab/GraphCL Paper: https://arxiv.org/abs/2010.13902

对于当前的图神经网络(GNNs)来说,图结构数据的可泛化、可迁移和鲁棒表示学习仍然是一个挑战。与为图像数据而开发的卷积神经网络(CNNs)不同,自监督学习和预训练很少用于GNNs。在这篇文章中,我们提出了一个图对比学习(GraphCL)框架来学习图数据的无监督表示。我们首先设计了四种类型的图扩充来包含不同的先验。然后,我们在四种不同的环境下系统地研究了图扩充的各种组合对多个数据集的影响:半监督、无监督、迁移学习和对抗性攻击。结果表明,与最先进的方法相比,即使不调优扩展范围,也不使用复杂的GNN架构,我们的GraphCL框架也可以生成类似或更好的可泛化性、可迁移性和健壮性的图表示。我们还研究了参数化图增强的范围和模式的影响,并在初步实验中观察了性能的进一步提高。

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Statistical inference with nonresponse is quite challenging, especially when the response mechanism is nonignorable. In this case, the validity of statistical inference depends on untestable correct specification of the response model. To avoid the misspecification, we propose semiparametric Bayesian estimation in which an outcome model is parametric, but the response model is semiparametric in that we do not assume any parametric form for the nonresponse variable. We adopt penalized spline methods to estimate the unknown function. We also consider a fully nonparametric approach to modeling the response mechanism by using radial basis function methods. Using Polya-gamma data augmentation, we developed an efficient posterior computation algorithm via Gibbs sampling in which most full conditional distributions can be obtained in familiar forms. The performance of the proposed method is demonstrated in simulation studies and an application to longitudinal data.

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