Tang, “Infograph: Unsupervised and semi-supervised graphlevel representation learning via mutual information maximization,” arXiv preprint arXiv:1908.01000, 2019. [7] F. Manessi and A. Rozza, “Graph-based neural network models with multiple self-supervised auxiliary tasks,” arXiv preprint arXiv...
我们分别使用AUROC和weight F1 score作为癫痫检测和分类的主要评估指标。表2见下图,显示了我们的DCRNN(no self-supervised pre-training)和基线的性能。无需self-supervised pre-training的distance graph-based的DCRNN(Dist DCRNN)和correlation graph-based的DCNN(Corr DCRNN)其性能与基线相当或更好。 abbr: w/o---...
We present InfoMotif, a new semi-supervised, motif-regularized, learning framework over graphs. We overcome two key limitations of message passing in popular graph neural networks (GNNs): localization (a k-layer GNN cannot utilize features outside the k-hop neighborhood of the labeled training ...
3.节点表示容易收到噪声交互的影响。在本文中作者通过在用户-物品图上引入自监督学习来改善GCN在推荐系统上的准确率和鲁棒性,将其称为Self-supervised Graph Learning(SGL),并应用在LightGCN模型上。SGL是模型无关的,并通过辅助自监督任务来补充监督任务中的信息以达成上述目的。
Self-supervised learningHeterogeneous graph neural networksGraph contrastive learningSelf-supervised heterogeneous graph neural networks have shown remarkable effectiveness in addressing the challenge of limited labeled data. However, current contrastive learning methods face limitations in leveraging neighborhood ...
Graph Self-Supervised Learning: A Survey 作者:Philip S. Yu等 主要工作: 对图自监督学习进行归类 总结了已有的图自监督学习的工作 提出了对后续的图自监督学习工作方向的展望 相较于已有的图自监督学习综述,他们的工作对这块分得更科学更细致 Introduction 部分 ...
[arXiv 2023] Structure-Aware Group Discrimination with Adaptive-View Graph Encoder: A Fast Graph Contrastive Learning Framework [paper] [TNNLS 2023] Self-supervised Learning IoT Device Features with Graph Contrastive Neural Network for Device Classification in Social Internet of Things [paper] [TKDE ...
In this paper, we introduce FlyGCL, a self-supervised learning approach designed to automatically learn neuron-level circuit networks, enabling the capture of the connectome's topological feature. Specifically, we leverage graph augmentation methods to generate various contrastive graph views. The ...
deep-learningscalabilitylink-predictionself-supervisiongraph-neural-networksself-supervised-learninggnnefficient-trainingnon-contrastive-learning UpdatedJan 13, 2023 Python Self-Supervised RGBD Reconstruction from Brain Activity image-reconstructionfmridepth-estimationmind-readingself-supervisionbrain-decoding ...
supervised learning在过去的十年里取得了巨大的成功,但是他对数据的依赖和易于攻击的特点促使人们寻找更好的解决方案。作为一种替代方法,自监督学习(SSL)近年来因其在表示学习方面的卓越表现而吸引了许多研究者。自监督表示学习利用输入数据本身作为监督,并且几乎有利于所有类型的下游任务。在这个调查中,我们看看新的自我...