随笔分类 - Neural Networks
摘要：目录概符号说明Empirical AnalysisSkeleton GraphNode FetchingGraph Condensation代码 Cao L., Deng H., Wang C., Chen L. and Yang Y. Graph-skeleton: ~1% nodes are s
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摘要：目录概符号说明GenAgg代码 Kortvelesy R., Morad S. and Prorok A. Generalised f-mean aggregation for graph neural networks. NIPS, 2023. 概 基于 MPNN 架构的 GNN 主要在于 agg
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摘要：目录概符号说明Cold Brew代码 Zheng W., Huang E. W., Rao N., Katariya S., Wang Z., Subbian K. Cold brew: Distilling graph node representations with incomplete or
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摘要：目录概符号说明MotivationFavardGNN代码 Guo Y. and Wei Z. Graph neural networks with learnable and optimal polynomial bases. ICML, 2023. 概 自动学多项式基的谱图神经网络. 符号说明 \
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摘要：目录概符号说明MotivationChebNetII代码 He M., Wei Z. and Wen J. Convolutional neural networks on graphs with chebyshev approximation, revisited. NIPS, 2022. 概 作
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摘要：目录概MotivationMarked Temporal Point Process代码 Du N., Dai H., Trivedi R., Upadhyay U., Gomez-Rodriguze M. and Song L. Recurrent marked temporal point pr
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摘要：目录概符号说明LLP代码 Guo Z., Shiao W., Zhang S., Liu Y., Chawla N. V., Shah N. and Zhao T. Linkless link prediction via relational distillation. ICML, 2023. 概
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摘要：目录概符号说明DistillGCNLocal Structure Preserving代码 Yang Y., Qiu J., Song M., Tao D. and Wang X. Distilling knowledge from graph convolutional networks. CVP
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摘要：目录概符号说明DKD代码 Zhao B., Cui Q., Song R., Qiu Y. and Liang J. Decoupled knowledge distillation. CVPR, 2022. 概 对普通的 KD (Knowledge Distillation) 损失解耦得到 Tar
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摘要：目录概主要内容代码 Jiang J., Zhou K., Zhao W. and Wen J. UniKGQA: Unified retrieval and reasoning for solving multi-hop question answering over knowledge graph
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摘要：目录概主要内容代码 Sun H., Dhingra B., Zaheer M., Mazaitis K., Salakhutdinov R. and Cohen W. W. Open domain question answering using early fusion of knowledge
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摘要：[TOC] > [Su J., Lu Y., Pan S., Murtadha A., Wen B. and Liu Y. RoFormer: Enhanced transformer with rotary position embedding. ](http://arxiv.org/abs/21
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摘要：[TOC] > [Zhang B. and Sennrich R. Root mean square layer normalization. NIPS, 2019.](http://arxiv.org/abs/1910.07467) ## 概 RMSNorm 节省时间. ## RMSNorm -
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摘要：[TOC] > [Yang Y., Liu T., Wang Y., Zhou J., Gan Q., Wei Z., Zhang Z., Huang Z. and Wipf D. Graph neural networks inspired by classical iterative algor
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摘要：[TOC] > [Wu Q., Yang C., Zhao W., He Y., Wipf D. and Yan J. DIFFormer: Scalable (graph) transformers induced by energy constrained diffusion. ICLR, 20
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摘要：Ying C., Cai T., Luo S., Zheng S., Ke D., Shen Y. and Liu T. Do transformers really perform badly for graph representation? NIPS, 2021. 概 本文提出了一种基于图的
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摘要：Chen L., Li J., Peng Q., Liu Y., Zheng Z. and Yang C. Understanding structural vulnerability in graph convolutional networks. IJCAI, 2021. 概 mean 是在 G
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摘要：Zhao L. and Akoglu L. Pairnorm: tackling oversmoothing in gnns. ICLR, 2020. 概 本文提出了一种 normaliztion 方法用于解决 over-smoothing 问题. 符号说明 $\mathcal{G} = (\mat
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摘要：Li G., Xiong C., Thabet A. and Ghanem B. DeeperGcn: all you need to train deeper gcns. arXiv preprint arXiv:2006.07739 概 本文介绍了一种连续可微的 aggregation func
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摘要：Liu M., Gao H. and Ji S. Towards deeper graph neural networks. KDD, 2020. 概 本文介绍了一种加深模型的方法. 符号说明 , 图; ; ; $\bm{A} \in \m
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