Molecular Generative Adversarial Networks (MolGAN):将relational GCN、改进的GAN和强化学习(RL)目标集成在一起,以生成具有所需属性的图。GAN由一个生成器和一个鉴别器组成,它们相互竞争以提高生成器的真实性。在MolGAN中,生成器试图提出一个伪图及其特征矩阵,而鉴别器的目标是区分伪样本和经验数据。此外,还引入了一...
代表性的无监督学习方法包括自动编码器(Auto-Encoders,AEs)和生成对抗网络(Generative Adversarial Networks,GANs)。基于 AE 和 GAN,许多方法通过学习「变换共变表征」(Transformation Equivariant Representations,TERs)来进一步提高无监督特征学习的质量。在 TER 学习中,通常假定在数据上施加变换会引起数据特征空间的共变,...
Molecular Generative Adversarial Networks (MolGAN): 将relational GCN、改进的GAN和强化学习(RL)目标集成在一起,以生成具有所需属性的图。GAN由一个生成器和一个鉴别器组成,它们相互竞争以提高生成器的真实性。在MolGAN中,生成器试图提出一个伪图及其特征矩阵,而鉴别器的目标是区分伪样本和经验数据。此外,还引入了...
To address the two issues, the deep graph convolutional generative adversarial network (DGCGAN), a model combining GCN and deep convolutional generative adversarial networks (DCGAN), is proposed in this paper. First, the graph data is mapped to a highly nonlinear space by using the topo...
GraphGAN[2018] : GraphGAN: Graph Representation Learning with Generative Adversarial Nets 如果评出最近两年最火的模型是什么?那么GAN一定在候选名单中,GAN和VAE是目前最优秀也是最经常拿来比较的两个深度生成模型,顺便提一句,最近有一篇paper: Variational Inference: A Unified Framework of Generative Models and So...
learning models, such as generative adversarial networks (GAN) and convolutional neural networks (CNN) for pose prediction and energy evaluation, respectively... DD Nguyen,K Gao,M Wang,... - Journal of Computer-Aided Molecular Design 被引量: 0发表: 2019年 Advanced Deep Learning Models for 6G:...
Adversarial Network Embedding Quanyu Dai, Qiang Li, Jian Tang, Dan Wang AAAI 2018 GraphGAN: Graph Representation Learning with Generative Adversarial Nets Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, Minyi Guo ...
Includes adversarial attacks with Graph Convol… streaming timeseries time-series lstm generative-adversarial-network gan rnn autoencoder ensemble-learning trees active-learning concept-drift graph-convolutional-networks interpretability anomaly-detection adversarial-attacks explaination anogan unsuperivsed nettack ...
Data augmentation for graph neural networksData augmentation generative adversarial networks https://zhuanlan.zhihu.com/p/389386601 --- 论文信息 1 Introduction 2 Local Augmentation 2.1 Learning The Conditional Distribution 2.2 The Architecture of LA-GNN 2.3 Active Learning 3 Discussion 4 Experiments 5 Co...
2.2.2 Generative Adversarial Networks 生成对抗网络 (GAN) 是一种机器学习框架,具有两个不同的网络作为生成器和判别器,在生成和检测假样本时进行博弈。 此外,对于不同的应用,可能需要从头开始构建 GAN,而我们的方法是一种通用的解决方案,可以无缝地应用来增强现有的图神经网络模型,同时减少计算和调整开销。