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IP属地：北京
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论文笔记之AM-GCN: Adaptive Multi-channel Graph Convolutional NetworksAM-GCN: Adaptive Multi-channel Graph Convolutional Networks 文中提出在复杂网络中，现...
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论文笔记之Deep Anomaly Detection on Attributed Networks
Deep Anomaly Detection on Attributed Networks 提出了DOMINANT(Deep Anomaly D...
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论文笔记之Time Series Shapelets: A New Primitive for Data MiningTime Series Shapelets: A New Primitive for Data Mining 对于时序数据，提出了一种新的特征，...
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论文笔记之Dynamic Heterogeneous Graph Neural Network for Real-time Event PredictionDynamic Heterogeneous Graph Neural Network for Real-time Event Predictio...
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论文笔记之GPT-GNN: Generative Pre-Training of Graph Neural NetworksGPT-GNN: Generative Pre-Training of Graph Neural Networks 文中指出训练GNN需要大量和...
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论文笔记之Deep Neural Networks for YouTube RecommendationsDeep Neural Networks for YouTube Recommendations 文中把整个推荐过程分成两个步骤：•deep c...
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论文笔记之xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender SystemsxDeepFM: Combining Explicit and Implicit Feature Interactions for Recomm...
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论文笔记之Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction
Feature Generation by Convolutional Neural Network for Click-Through Rat...
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论文笔记之A Convolutional Click Prediction ModelA Convolutional Click Prediction Model 目标：CTR预估 Convolution Layer 对于有n个元...