Multi-Task Graph Autoencoders 来自 arXiv.org 喜欢 0 阅读量: 46 作者: PV Tran 摘要: We examine two fundamental tasks associated with graph representation learning: link prediction and node classification. We present a new autoencoder architecture capable of learning a joint representation of local...
In this paper, we propose a training framework named MTDA (Multi-Task learning with Data Augmentation)-GNN, which combines data augmentation and multi-task learning to improve the node classification performance of GNN on small-scale graph data. First, we use Graph Auto-Encoders (GAE) as a ...
For example, GCN (Kipf & Welling, 2016a) uses graph convolutional layers and the softmax layer to predict node classes and Graph Auto-Encoders (GAE) (Kipf & Welling, 2016b) first uses the graph convolution layer to obtain the graph embedding, and then inner-products the embedding to ...
Autoencoder (AE) based methods have been widely used for unsupervised ASD, but suffer from problems including 'shortcut', poor anti-noise ability and sub-optimal quality of features. To address these challenges, we propose a new AE-based framework termed AEGM. Specifically, we first insert an...
由于它们和主任务密切相关,所以在学习的同时可能允许这些模型学到有利于主任务的表示。一个更为显式的做法是利用一个辅助任务专门来学习一个可以迁移的表示。Cheng等人2015年的一个工作以及文献[50]所采用的语言模型目标就起到了这样的作用。类似的,autoencoder也是可以用来做辅助任务的。
由于它们和主任务密切相关,所以在学习的同时可能允许这些模型学到有利于主任务的表示。一个更为显式的做法是利用一个辅助任务专门来学习一个可以迁移的表示。Cheng等人2015年的一个工作以及文献[50]所采用的语言模型目标就起到了这样的作用。类似的,autoencoder也是可以用来做辅助任务的。
由于它们和主任务密切相关,所以在学习的同时可能允许这些模型学到有利于主任务的表示。一个更为显式的做法是利用一个辅助任务专门来学习一个可以迁移的表示。Cheng等人2015年的一个工作以及文献[50]所采用的语言模型目标就起到了这样的作用。类似的,autoencoder也是可以用来做辅助任务的。
Multitask joint learning with graph autoencoders for predicting potential MiRNA-drug associations Multitask learning can effectively enhance the prediction performance of a single task by extending the valid information of related tasks. In this paper, ... Y Zhong,C Shen,Ding P.Luo L.Xi X.Luo...
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