2.1 GAUG-M for Modified-Graph Setting 基于上文中作者为图数据增强任务所定义的两种策略,首先介绍为 modified-graph setting 设计的 GAUG-M 方法,包含以下两个步骤: 使用edge predictor 来获得 \mathcal{G} 中所有可能的和现有 edge 的 edge probabilities。 edge predictor 的作用是灵活的,通常可以用任何合适的...
1)基于原始节点特 征构建graphon节点特征, 2)基于graphon节点特征生成合成图节点特征。 具体来说, 在graphon估计阶段, 我们在对齐邻接矩阵的同时对齐原始节点特征, 因此我们对每个graphon都有一组对齐的原始节点 特征, 然后我们对对齐的原始节点特征进行池化(在我们的实验中是平均池化), 获得图形节点特征。生成的图...
在本文中,我们提出了一个新的框架Metropolis-Hastings数据增强(MH-Aug,Metropolis-Hastings Data Augmentation),该框架从显式目标分布中(from an explicit target distribution)绘制增强图,用于半监督学习。MH-Aug根据目标分布生成一系列增强图,从而能够灵活控制增强的强度和多样性。由于从复杂目标分布中直接采样具有挑战性,...
Neural networks have become a powerful tool in pattern recognition and part of their success is due to generalization from using large datasets. However, unlike other domains, time series classification datasets are often small. In order to address this problem, we propose a novel time series dat...
论文标题:SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation论文作者:Jun Xia, Lirong Wu, Jintao Chen, Bozhen Hu, Stan Z. Li论文来源:2022, WWW论文地址:download 论文代码:download 1 Introduction对比学习种数据增强存在的三个问题:...
Task: use fuzzing to augment the training data of DNNs Method: 将data augmentation program整体视作一个优化任务,用genetic search来生成input data, Q: 试图识别能够skipping augmentation的机会来加速 实验: datasets: GTSRB(1,2,3,4), FM(1,2,3), CIFAR-10(1,2,3,4), IMDB(1,2), SVHN(1,2)...
几篇论文实现代码:《Learning Data Manipulation for Augmentation and Weighting》(NeurIPS 2019) GitHub: http://t.cn/AiBvbwNo 《Learning to Predict Without Looking Ahead: World Models Without Forward...
中国大模型语料数据联盟开源数据服务指定平台。为大模型提供多种类高质量的开放数据集,已覆盖数百种任务类型的数千个数据集。
While convolutional neural networks (CNNs) have been successfully applied to many challenging classification applications, they typically require large datasets for training. When the availability of labeled data is limited, data augmentation is a critical preprocessing step for CNNs. However, data augmen...
代码 GitHub - junxia97/SimGRACE: [WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation"github.com/junxia97/SimGRACE 核心要点 文章旨在解决现有在图对比学习中需要依赖大量试验和人工经验针对数据集构造增广视图,并且可能在增广不当时导致语义变化的问题,基于扰动...