课程介绍(A001047)：
深蓝 人工智能 深蓝图卷积神经网络
文件目录：
| 深蓝 人工智能 深蓝图卷积神经网络 |
| │ ├─ |
| │ ├─图卷积神经网络开课仪式.pptx 362.72KB |
| │ ├─图神经网络（GNN）100篇论文集 |
| │ │ ├─Applications |
| │ │ │ ├─combinatorial optimization |
| │ │ │ │ ├─Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search(1).pdf 718.71KB |
| │ │ │ │ └─Learning Combinatorial Optimization Algorithms over Graphs.pdf 3.09MB |
| │ │ │ ├─graph generation |
| │ │ │ │ ├─Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation.pdf 701.09KB |
| │ │ │ │ ├─MolGAN- An implicit generative model for small molecular graphs(1).pdf 1.27MB |
| │ │ │ │ └─NetGAN- Generating Graphs via Random Walks(1).pdf 1.8MB |
| │ │ │ ├─image |
| │ │ │ │ ├─Image classification |
| │ │ │ │ │ └─Few-Shot Learning with Graph Neural Networks.pdf 1.86MB |
| │ │ │ │ ├─Interaction Detection |
| │ │ │ │ │ └─Structural-RNN- Deep Learning on Spatio-Temporal Graphs.pdf 1.27MB |
| │ │ │ │ ├─Object Detection |
| │ │ │ │ │ ├─Learning Region features for Object Detection.pdf 1.86MB |
| │ │ │ │ │ └─Relation Networks for Object Detection.pdf 1.06MB |
| │ │ │ │ ├─Region Classification |
| │ │ │ │ │ └─Iterative Visual Reasoning Beyond Convolutions..pdf 4.08MB |
| │ │ │ │ ├─Semantic Segmentation |
| │ │ │ │ │ ├─3D Graph Neural Networks for RGBD Semantic Segmentation.pdf 2.4MB |
| │ │ │ │ │ ├─Dynamic Graph CNN for Learning on Point Clouds.pdf 5.25MB |
| │ │ │ │ │ ├─Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs.pdf 5MB |
| │ │ │ │ │ ├─Modeling polypharmacy side effects with graph convolutional networks.pdf 4.28MB |
| │ │ │ │ │ └─PointNet- Deep Learning on Point Sets for 3D Classification and Segmentation.pdf 8.84MB |
| │ │ │ │ ├─Social Relationship Understanding |
| │ │ │ │ └─Visual Question Answering |
| │ │ │ │ ├─Graph-Structured Representations for Visual Question Answering.pdf 3.92MB |
| │ │ │ │ └─Out of the Box- Reasoning with Graph Convolution Nets for Factual Visual Question Answering(1).pdf 2.62MB |
| │ │ │ ├─knowledge graph |
| │ │ │ │ ├─Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks.pdf 610.44KB |
| │ │ │ │ ├─Deep Reasoning with Knowledge Graph for Social Relationship Understanding.pdf 2.93MB |
| │ │ │ │ ├─Dynamic Graph Generation Network- Generating Relational Knowledge from Diagrams.pdf 1.37MB |
| │ │ │ │ ├─Knowledge Transfer for Out-of-Knowledge-Base Entities – A Graph Neural Network Approach.pdf 531.96KB |
| │ │ │ │ ├─Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering.pdf 618.29KB |
| │ │ │ │ ├─Multi-Label Zero-Shot Learning with Structured Knowledge Graphs.pdf 1.54MB |
| │ │ │ │ ├─Representation learning for visual-relational knowledge graphs.pdf 7.08MB |
| │ │ │ │ ├─The More You Know- Using Knowledge Graphs for Image Classification.pdf 2.48MB |
| │ │ │ │ └─Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs.pdf 1.8MB |
| │ │ │ ├─science |
| │ │ │ │ ├─A Compositional Object-Based Approach to Learning Physical Dynamics.pdf 4.44MB |
| │ │ │ │ ├─A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks.pdf 517.84KB |
| │ │ │ │ ├─A simple neural network module for relational reasoning.pdf 1.55MB |
| │ │ │ │ ├─Action Schema Networks- Generalised Policies with Deep Learning.pdf 1.84MB |
| │ │ │ │ ├─Adversarial Attack on Graph Structured Data.pdf 770.47KB |
| │ │ │ │ ├─Attend, Infer, Repeat- Fast Scene Understanding with Generative Models.pdf 1.48MB |
| │ │ │ │ ├─Attention, Learn to Solve Routing Problems!.pdf 1.67MB |
| │ │ │ │ ├─Beyond Categories- The Visual Memex Model for Reasoning About Object Relationships.pdf 797.68KB |
| │ │ │ │ ├─Combining Neural Networks with Personalized PageRank for Classification on Graphs.pdf 666.15KB |
| │ │ │ │ ├─Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders.pdf 744.82KB |
| │ │ │ │ ├─Constructing Narrative Event Evolutionary Graph for Script Event Prediction.pdf 829KB |
| │ │ │ │ ├─Conversation Modeling on Reddit using a Graph-Structured LSTM.pdf 867.19KB |
| │ │ │ │ ├─Convolutional networks on graphs for learning molecular fingerprints.pdf 964.62KB |
| │ │ │ │ ├─Cross-Sentence N-ary Relation Extraction with Graph LSTMs.pdf 723.8KB |
| │ │ │ │ ├─Deep Graph Infomax.pdf 8.33MB |
| │ │ │ │ ├─DeepInf- Modeling influence locality in large social networks.pdf 1.24MB |
| │ │ │ │ ├─Discovering objects and their relations from entangled scene representations.pdf 5.17MB |
| │ │ │ │ ├─Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs.pdf 746.41KB |
| │ │ │ │ ├─Effective Approaches to Attention-based Neural Machine Translation.pdf 428.27KB |
| │ │ │ │ ├─Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks.pdf 7.16MB |
| │ │ │ │ ├─Graph Convolutional Matrix Completion.pdf 907.15KB |
| │ │ │ │ ├─Graph Convolutional Neural Networks for Web-Scale Recommender Systems.pdf 10.01MB |
| │ │ │ │ ├─Graph networks as learnable physics engines for inference and control.pdf 2.9MB |
| │ │ │ │ ├─GraphRNN- Generating Realistic Graphs with Deep Auto-regressive Models.pdf 2.61MB |
| │ │ │ │ ├─Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification.pdf 2.69MB |
| │ │ │ │ ├─Hyperbolic Attention Networks.pdf 3.26MB |
| │ │ │ │ ├─Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks.pdf 489.27KB |
| │ │ │ │ ├─Inference in Probabilistic Graphical Models by Graph Neural Networks.pdf 3.24MB |
| │ │ │ │ ├─Interaction Networks for Learning about Objects, Relations and Physics.pdf 2.08MB |
| │ │ │ │ ├─Learning a SAT Solver from Single-Bit Supervision.pdf 2.07MB |
| │ │ │ │ ├─Learning Conditioned Graph Structures for Interpretable Visual Question Answering.pdf 8.66MB |
| │ │ │ │ ├─Learning Deep Generative Models of Graphs.pdf 2.45MB |
| │ │ │ │ ├─Learning Graphical State Transitions.pdf 1.66MB |
| │ │ │ │ ├─Learning Human-Object Interactions by Graph Parsing Neural Networks.pdf 4.08MB |
| │ │ │ │ ├─Learning model-based planning from scratch.pdf 1.46MB |
| │ │ │ │ ├─Learning Multiagent Communication with Backpropagation.pdf 4.18MB |
| │ │ │ │ ├─Learning to Represent Programs with Graphs.pdf 607.09KB |
| │ │ │ │ ├─Metacontrol for Adaptive Imagination-Based Optimization.pdf 1.79MB |
| │ │ │ │ ├─Molecular Graph Convolutions- Moving Beyond Fingerprints.pdf 2.28MB |
| │ │ │ │ ├─NerveNet Learning Structured Policy with Graph Neural Networks.pdf 3.31MB |
| │ │ │ │ ├─Neural Combinatorial Optimization with Reinforcement Learning.pdf 582.63KB |
| │ │ │ │ ├─Neural Module Networks.pdf 1.21MB |
| │ │ │ │ ├─Neural Relational Inference for Interacting Systems.pdf 3MB |
| │ │ │ │ ├─Protein Interface Prediction using Graph Convolutional Networks.pdf 1016.82KB |
| │ │ │ │ ├─Relational Deep Reinforcement Learning.pdf 6.99MB |
| │ │ │ │ ├─Relational inductive bias for physical construction in humans and machines.pdf 1.17MB |
| │ │ │ │ ├─Relational neural expectation maximization- Unsupervised discovery of objects and their interactions.pdf 1.32MB |
| │ │ │ │ ├─Self-Attention with Relative Position Representations.pdf 404.98KB |
| │ │ │ │ ├─Semi-supervised User Geolocation via Graph Convolutional Networks.pdf 1.31MB |
| │ │ │ │ ├─Situation Recognition with Graph Neural Networks.pdf 5.45MB |
| │ │ │ │ ├─Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition.pdf 1.67MB |
| │ │ │ │ ├─Spatio-Temporal Graph Convolutional Networks- A Deep Learning Framework for Traffic Forecasting.pdf 1.05MB |
| │ │ │ │ ├─Structured Dialogue Policy with Graph Neural Networks.pdf 965.29KB |
| │ │ │ │ ├─Symbolic Graph Reasoning Meets Convolutions.pdf 3.41MB |
| │ │ │ │ ├─Traffic Graph Convolutional Recurrent Neural Network- A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting.pdf 1.64MB |
| │ │ │ │ ├─Translating Embeddings for Modeling Multi-relational Data.pdf 588.79KB |
| │ │ │ │ ├─Understanding Kin Relationships in a Photo.pdf 1.62MB |
| │ │ │ │ ├─VAIN- Attentional Multi-agent Predictive Modeling.pdf 608.35KB |
| │ │ │ │ └─Visual Interaction Networks- Learning a Physics Simulator from Vide.o.pdf 5.58MB |
| │ │ │ └─text |
| │ │ │ ├─A Graph-to-Sequence Model for AMR-to-Text Generation.pdf 470.98KB |
| │ │ │ ├─Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling.pdf 789.3KB |
| │ │ │ ├─End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures.pdf 546.26KB |
| │ │ │ ├─Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks.pdf 784.41KB |
| │ │ │ ├─Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks..pdf 628.84KB |
| │ │ │ ├─Graph Convolution over Pruned Dependency Trees Improves Relation Extraction.pdf 962.87KB |
| │ │ │ ├─Graph Convolutional Encoders for Syntax-aware Neural Machine Translation.pdf 523.78KB |
| │ │ │ ├─Graph Convolutional Networks for Text Classification.pdf 2MB |
| │ │ │ ├─Graph Convolutional Networks with Argument-Aware Pooling for Event Detection.pdf 506.13KB |
| │ │ │ ├─Jointly Multiple Events Extraction via Attention-based Graph.pdf 609.6KB |
| │ │ │ ├─N-ary relation extraction using graph state LSTM.pdf 633.56KB |
| │ │ │ ├─Recurrent Relational Networks.pdf 482.13KB |
| │ │ │ ├─Sequence Labeling |
| │ │ │ └─Text classification |
| │ │ ├─Models |
| │ │ │ ├─graphtype |
| │ │ │ │ ├─Adaptive Graph Convolutional Neural Networks.pdf 980.99KB |
| │ │ │ │ ├─directed graph |
| │ │ │ │ │ └─Rethinking Knowledge Graph Propagation for Zero-Shot Learning.pdf 4.38MB |
| │ │ │ │ ├─edge-informative graph |
| │ │ │ │ │ ├─Graph-to-Sequence Learning using Gated Graph Neural Networks.pdf 4.23MB |
| │ │ │ │ │ └─Modeling relational data with graph convolutional networks.pdf 505.33KB |
| │ │ │ │ ├─Graph Capsule Convolutional Neural Networks.pdf 2.11MB |
| │ │ │ │ ├─Graph Neural Networks for Object Localization.pdf 397.84KB |
| │ │ │ │ ├─Graph Neural Networks for Ranking Web Pages.pdf 1.18MB |
| │ │ │ │ ├─Graph Partition Neural Networks for Semi-Supervised Classification.pdf 894KB |
| │ │ │ │ ├─heterogeneous graphs |
| │ │ │ │ ├─How Powerful are Graph Neural Networks-.pdf 871.25KB |
| │ │ │ │ ├─Mean-field theory of graph neural networks in graph partitioning.pdf 550.54KB |
| │ │ │ │ └─Spectral Networks and Locally Connected Networks on Graphs.pdf 2.04MB |
| │ │ │ ├─others |
| │ │ │ │ ├─A Comparison between Recursive Neural Networks and Graph Neural Networks.pdf 427.38KB |
| │ │ │ │ ├─A new model for learning in graph domains.pdf 356.89KB |
| │ │ │ │ ├─CelebrityNet- A Social Network Constructed from Large-Scale Online Celebrity Images.pdf 16.52MB |
| │ │ │ │ ├─Contextual Graph Markov Model- A Deep and Generative Approach to Graph Processing.pdf 750.55KB |
| │ │ │ │ ├─Deep Sets.pdf 5.3MB |
| │ │ │ │ ├─Deriving Neural Architectures from Sequence and Graph Kernels.pdf 880.73KB |
| │ │ │ │ ├─Diffusion-Convolutional Neural Networks.pdf 545.23KB |
| │ │ │ │ └─Geometric deep learning on graphs and manifolds using mixture model cnns.pdf 7.41MB |
| │ │ │ ├─propagationtype |
| │ │ │ │ ├─attention |
| │ │ │ │ │ ├─Attention Is All You Need.pdf 2.22MB |
| │ │ │ │ │ ├─Graph Attention Networks.pdf 1.66MB |
| │ │ │ │ │ └─Graph Classification using Structural Attention.pdf 2.64MB |
| │ │ │ │ ├─convolution |
| │ │ │ │ │ ├─Bayesian Semi-supervised Learning with Graph Gaussian Processes.pdf 872.04KB |
| │ │ │ │ │ ├─Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering.pdf 627.75KB |
| │ │ │ │ │ ├─Deep Convolutional Networks on Graph-Structured Data.pdf 4.75MB |
| │ │ │ │ │ ├─Learning Convolutional Neural Networks for Graphs.pdf 823.63KB |
| │ │ │ │ │ ├─Spectral Networks and Deep Locally Connected.pdf 2.04MB |
| │ │ │ │ │ └─Structure-Aware Convolutional Neural Networks.pdf 1.53MB |
| │ │ │ │ ├─gate |
| │ │ │ │ │ ├─Gated Graph Sequence Neural Networks.pdf 931.94KB |
| │ │ │ │ │ └─Sentence-State LSTM for Text Representation.pdf 620.68KB |
| │ │ │ │ └─skip |
| │ │ │ │ ├─Representation Learning on Graphs with Jumping Knowledge Networks.pdf 3.33MB |
| │ │ │ │ └─Semi-Supervised Classification with Graph Convolutional Networks.pdf 1.01MB |
| │ │ │ └─training methods |
| │ │ │ ├─boosting |
| │ │ │ │ └─Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning.pdf 2.14MB |
| │ │ │ ├─Covariant Compositional Networks For Learning Graphs.pdf 669.84KB |
| │ │ │ ├─Graphical-Based Learning Environments for Pattern Recognition.pdf 521.43KB |
| │ │ │ ├─Hierarchical Graph Representation Learning with Differentiable Pooling.pdf 2.49MB |
| │ │ │ ├─Knowledge-Guided Recurrent Neural Network Learning for Task-Oriented Action Prediction.pdf 1.15MB |
| │ │ │ ├─Learning Steady-States of Iterative Algorithms over Graphs.pdf 3.27MB |
| │ │ │ ├─neighborhood sampling |
| │ │ │ │ ├─Adaptive Sampling Towards Fast Graph Representation Learning.pdf 760.23KB |
| │ │ │ │ ├─FastGCN- Fast Learning with Graph Convolutional Networks via Importance Sampling.pdf 544.03KB |
| │ │ │ │ └─Inductive Representation Learning on Large Graphs.pdf 1.22MB |
| │ │ │ ├─Neural networks for relational learning- an experimental comparison.pdf 1.36MB |
| │ │ │ └─receptive field control |
| │ │ │ └─Stochastic Training of Graph Convolutional Networks with Variance Reduction.pdf 1.44MB |
| │ │ └─Survey |
| │ │ ├─一般推荐 |
| │ │ │ ├─A Comprehensive Survey on Graph Neural Networks.pdf 1.98MB |
| │ │ │ ├─Computational Capabilities of Graph Neural Networks(1).pdf 1.48MB |
| │ │ │ ├─Deep Learning on Graphs- A Survey.pdf 1.96MB |
| │ │ │ ├─Geometric Deep Learning- Going beyond Euclidean data.pdf 5.44MB |
| │ │ │ └─Neural Message Passing for Quantum Chemistry.pdf 694.46KB |
| │ │ └─极力推荐 |
| │ │ ├─Graph Neural Networks：A Review of Methods and Applications.pdf 2.85MB |
| │ │ ├─Non-local Neural Networks.pdf 1.41MB |
| │ │ ├─Relational Inductive Biases, Deep Learning, and Graph Networks.pdf 9.14MB |
| │ │ └─The Graph Neural Network Model.pdf 1.62MB |
| │ ├─第1章 从欧几里得空间到非欧几里得空间 |
| │ │ ├─Chapter1卷积神经网络-从欧式空间到非欧式空间.mp4 239.53MB |
| │ │ └─GCN第一节课.pdf 2.1MB |
| │ ├─第2章 谱域图卷积介绍 |
| │ │ ├─第2章谱域图卷积介绍.mp4 325.09MB |
| │ │ └─第二节课-谱域图卷积.pdf 3.59MB |
| │ ├─第3章 空域图卷积介绍 |
| │ │ ├─3.1-3.2 空域卷积.mp4 357.35MB |
| │ │ ├─3.1-3.2-3.3-3.4–L3空域图卷积介绍（一）.pdf 2.43MB |
| │ │ ├─3.3-3.4 空域卷积.mp4 133.48MB |
| │ │ ├─3.5-3.6-v5.0过平滑现象.pdf 2.66MB |
| │ │ ├─3.5图卷积网络回顾 空域图卷积2.mp4 164.45MB |
| │ │ └─3.6过平滑现象.mp4 310.82MB |
| │ ├─第4章 图卷积的实践应用 |
| │ │ ├─图卷积神经网络的应用.mp4 461.09MB |
| │ │ └─第五节课.pdf 3.34MB |
| │ ├─第5章 实践：基于PyG的图卷积的节点分类(1) |
| │ │ ├─19：第五章作业讲评.mp4 62.08MB |
| │ │ ├─保存模型与相关代码.zip 4.4MB |
| │ │ ├─实践作业.pdf 455.01KB |
| │ │ ├─第1节 环境搭建 |
| │ │ │ └─【视频】环境搭建.mp4 336.6MB |
| │ │ ├─第2节 基于PyG框架的节点分类实践 |
| │ │ │ ├─16：【视频】节点分类实践（上）.mp4 229.86MB |
| │ │ │ └─16：【视频】节点分类实践（下）.mp4 200.78MB |
| │ │ ├─第3节 构造自己的数据集&查阅其他GCN方法 |
| │ │ │ └─17：【视频】构造自己的数据集&查阅其他GCN方法.mp4 309.64MB |
| │ │ └─第4节 实践作业 |
| │ │ ├─第六次课.pdf 304.15KB |
| │ │ └─节点分类code.rar 2.09KB |
| │ └─第6章 实践：基于Pytorch的图卷积的交通预测 |
| │ ├─图卷积第6章优秀作业（PCCH）.zip 32.78MB |
| │ ├─第1节 课件&代码 |
| │ │ ├─code.rar 31.12MB |
| │ │ └─第七次课.pdf 420.48KB |
| │ ├─第2节 时序数据处理及建模 |
| │ │ └─20：【视频】时序数据处理及建模.mp4 349.96MB |
| │ ├─第3节 基于Pytorch的交通流量预测 |
| │ │ └─21：【视频】基于Pytorch的交通流量预测.mp4 427.72MB |
| │ └─第4节 作业 |
| │ └─作业.pdf 441.68KB |
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