├── README.md ├── distributed_systems ├── 1. Resilient Distributed Datasets A Fault-Tolerant Abstraction for In-Memory Cluster Computing(Spark).pdf ├── 2. Parameter Server for Distributed Machine Learning(Parameter Server).pdf ├── 3. Angel a new large-scale machine learning system(Angel).pdf ├── 4. Pregel A System for Large-scale Graph Processing(Pregel).pdf ├── 5. Distributed GraphLab A Framework for Machine Learning and Data Mining in the cloud(GraphLab).pdf ├── 6. PowerGraph Distributed Graph-Parallel Computation on Natural Graphs(PowerGraph).pdf ├── 7. Graphx Unifying Data-Parallel and Graph-Parallel Analytics(Graphx).pdf ├── 8. PSGraph How Tencent trains extremely large-scale graphs with Spark(PSGraph).pdf └── 9.Heterogeneity-aware Distributed Parameter Servers(Parameter Server).pdf ├── graph_embedding ├── DeepWalk Online Learning of Social Representations(DeepWalk).pdf ├── LINE Large-scale Information Network Embedding(LINE).pdf └── Metapath2Vec Scalable Representation Learning for Heterogeneous Networks(Metapath2Vec).pdf ├── graph_mining ├── 1.The PageRank Citation Ranking Bringing Order to the Web(Pagerank).pdf ├── 2.The H-index of a network node and its relation to degree and coreness(kcore hindex).pdf ├── 3.HyperAnf Approximating the Neighbourhood Function of Very Large Graphs on a Budget(HyperAnf).pdf └── 4.Centralities in Large Networks Algorithms and Observations(Closeness).pdf ├── graph_neural_network ├── DeepTrax Embedding Graphs of Financial Transactions(Financial).pdf ├── Graph Convolutional Neural Networks for Web-Scale Recommender Systems(Pinsage).pdf ├── Graph Neural Netowrks~ A Review of Methods and Applications(GNN综述).pdf ├── How Powerful Are Graph Neural Networks(GNN and WL Test).pdf ├── Inductive Representation Learning On Large Graphs(Graphsage).pdf └── Semi-Supervised Classification With Graph Convolutional Networks(GCN).pdf └── machine_learning ├── Ad Click Prediction ~ a View from the Trenches(FTRL).pdf ├── An Introduction to Logistic Regression Analysis and Reporting.pdf ├── Attentional Factorization Machines ~ Learning the Weighted of Feature Interactions via Attention Networks(AttentionFM).pdf ├── Deep & Cross Network for Ad Click Predictions(DCN).pdf ├── DeepFM A Factorization-Machine based Neural Network for CTR Prediction(DeepFM).pdf ├── DimBoost~ Boosting Gradient Boosting Decision Tree to Higher Dimensions(DimBoost).pdf ├── Factorization Machines(FM).pdf ├── LDA ~ A Robust and Large-scale Topic Modeling System(LDA).pdf ├── Product-based Neural Networks for User Response Prediction(PNN).pdf ├── Space-Efficient Online Computation of Quantile Summaries(Quantile Summaries).pdf ├── Web-Scale K-Means Clustering(Kmeans).pdf ├── Wide & Deep Learning for Recommender Systems(DeepAndWide).pdf ├── XGBoost ~ A Scalable Tree Boosting System(XGBoost).pdf └── xDeepFM~ Combining Explicit and Implicit Feature Interactions for Recommender Systems(xDeepFM).pdf /README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Angel-ML/papers-we-learn/HEAD/README.md -------------------------------------------------------------------------------- /distributed_systems/1. Resilient Distributed Datasets A Fault-Tolerant Abstraction for In-Memory Cluster Computing(Spark).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Angel-ML/papers-we-learn/HEAD/distributed_systems/1. Resilient Distributed Datasets A Fault-Tolerant Abstraction for In-Memory Cluster Computing(Spark).pdf -------------------------------------------------------------------------------- /distributed_systems/2. Parameter Server for Distributed Machine Learning(Parameter Server).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Angel-ML/papers-we-learn/HEAD/distributed_systems/2. Parameter Server for Distributed Machine Learning(Parameter Server).pdf -------------------------------------------------------------------------------- /distributed_systems/3. Angel a new large-scale machine learning system(Angel).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Angel-ML/papers-we-learn/HEAD/distributed_systems/3. Angel a new large-scale machine learning system(Angel).pdf -------------------------------------------------------------------------------- /distributed_systems/4. Pregel A System for Large-scale Graph Processing(Pregel).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Angel-ML/papers-we-learn/HEAD/distributed_systems/4. Pregel A System for Large-scale Graph Processing(Pregel).pdf -------------------------------------------------------------------------------- /distributed_systems/5. Distributed GraphLab A Framework for Machine Learning and Data Mining in the cloud(GraphLab).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Angel-ML/papers-we-learn/HEAD/distributed_systems/5. Distributed GraphLab A Framework for Machine Learning and Data Mining in the cloud(GraphLab).pdf -------------------------------------------------------------------------------- /distributed_systems/6. PowerGraph Distributed Graph-Parallel Computation on Natural Graphs(PowerGraph).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Angel-ML/papers-we-learn/HEAD/distributed_systems/6. PowerGraph Distributed Graph-Parallel Computation on Natural Graphs(PowerGraph).pdf -------------------------------------------------------------------------------- /distributed_systems/7. Graphx Unifying Data-Parallel and Graph-Parallel Analytics(Graphx).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Angel-ML/papers-we-learn/HEAD/distributed_systems/7. Graphx Unifying Data-Parallel and Graph-Parallel Analytics(Graphx).pdf -------------------------------------------------------------------------------- /distributed_systems/8. PSGraph How Tencent trains extremely large-scale graphs with Spark(PSGraph).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Angel-ML/papers-we-learn/HEAD/distributed_systems/8. PSGraph How Tencent trains extremely large-scale graphs with Spark(PSGraph).pdf -------------------------------------------------------------------------------- /distributed_systems/9.Heterogeneity-aware Distributed Parameter Servers(Parameter Server).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Angel-ML/papers-we-learn/HEAD/distributed_systems/9.Heterogeneity-aware Distributed Parameter Servers(Parameter Server).pdf -------------------------------------------------------------------------------- /graph_embedding/DeepWalk Online Learning of Social Representations(DeepWalk).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Angel-ML/papers-we-learn/HEAD/graph_embedding/DeepWalk Online Learning of Social Representations(DeepWalk).pdf -------------------------------------------------------------------------------- /graph_embedding/LINE Large-scale 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