随笔分类 - Representation Learning
摘要：[TOC] > [Zhou D., Bousquet O., Lal T. N., Weston J. and Scholk\ddot{o}pf B. Learning with local and global consistency. NIPS, 2004.](https://proceedin
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摘要：[TOC] > [Huang Q., He H., Singh A., Lim S. and Benson A. R. Combining label propagation and simple models out-performs graph neural networks. ICLR, 20
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摘要：Yuan H. and Ji S. Structpool: structured graph pooling via conditional random fields. ICLR, 2020. 概 一种图的 pooling 方法, 我并没有搞懂其中的原理, 这里只是记录一下. 符号说明 ,
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摘要：Graikos A., Malkin N., Jojic N. and Samaras D. Diffusion models as plug-and-play priors. NIPS, 2022. 概 有了先验分布 (用一般的扩散模型去拟合), 我们总是像添加一些
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摘要：Tang J. and Wang K. Personalized top-n sequential recommendation via convolutional sequence embedding. WSDM, 2018. 概 序列推荐的经典之作, 将卷积用在序列推荐之上. 符号说明 $\ma
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摘要：Ethayarajh K., Choi Y. and Swayamdipta S. Understanding dataset difficulty with -usable information. ICML, 2022. 概 将 -inform
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摘要：Gupta U., Ferber A. M., Dilkina B. and Steeg G. V. Controllable guarantees for fair outcomes via contrastive information estimation. AAAI, 2021. 概 本文提
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摘要：Ma Y., Wang S., Aggarwal C. C. and Tang J. Graph convolutional networks with eigenpooling. KDD, 2019. 概 本文提出了一种新的框架, 在前向的过程中, 可以逐步将相似的 nodes 和他们的特征聚合在
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摘要：Chiang W., Liu X., Si S., Li Y., Bengio S. and Hsieh C. Cluster-GCN: An efficient algorithm for training deep and large graph convolutional networks.
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摘要：Cen Y., Zou X., Zhang J., Yang H., Zhou J. and Tang J. Representation learning for attributed multiplex heterogeneous network. KDD, 2019. 概 本文在 Attrib
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摘要：Maron H., Ben-Hamu H., Shamir N. and Lipman Y. Invariant and equivariant graph networks. ICLR, 2019. 概 有些时候, 我们希望网络具有: 不变性 (Invariant): $$ f(PX) = f(X
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摘要：目录概符号说明MotivationLADIES代码 Zou D., Hu Z., Wang Y., Jiang S., Sun Y. and Gu Q. Layer-dependent importance sampling for training deep and large graph con
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摘要：Wang X., Ji H., Shi C., Wang B., Cui P., Yu P. and Ye Y. Heterogeneous graph attention network. WWW, 2019. 概 Attention + 异构图. 符号说明 $\mathcal{G} = (\ma
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摘要：Ren Y., Liu B., Huang C., Dai P., Bo L. and Zhang J. Heterogeneous deep graph infomax. arXiv preprint arXiv:1911.08538, 2019. 概 本文介绍了异构图的一种无监督学习方法. 这里
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摘要：目录概符号说明MotivationFastGCN方差分析代码 Chen J., Ma T. and Xiao C. FastGCN: fast learning with graph convolutional networks via importance sampling. ICLR, 2018
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摘要：Li Q., Han Z. and Wu X. Deeper insights into graph convolutional networks for semi-supervised learning. AAAI, 2018. 概 本文分析了 GCN 的实际上就是一种 Smoothing, 但是
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摘要：目录概符号说明Motivation本文方法代码 Chen J., Zhu J. and Song L. Stochastic training of graph convolutional networks with variance reduction. ICML, 2018. 概 我们都知道,
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摘要：Xu Y., Zhao S., Song J., Stewart R. and Ermon S. A theory of usable information under computational constraints. International Conference on Learning
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摘要：Gao Z., Guo J., Tan X., Zhu Y., Zhang F., Bian J. and Xu L. Difformer: Empowering diffusion models on the embedding space for text generation. arXiv p
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