WM134 – 图卷积神经网络 [4.2G]

WM134 – 图卷积神经网络 [4.2G]

┣━━01.第1章 卷积神经网络从欧几里得空间到非欧几里得空间 [240.8M]
┃ ┣━━Chapter1卷积神经网络-从欧式空间到非欧式空间.mp4 [238.9M]
┃ ┗━━GCN第一节课.pdf [1.9M]
┣━━02.第2章 谱域图卷积介绍 [327.8M]
┃ ┣━━第2章谱域图卷积介绍.mp4 [324.5M]
┃ ┗━━第二节课-谱域图卷积.pdf [3.3M]
┣━━03.第3章 空域图卷积介绍 [968.4M]
┃ ┣━━3.1-3.2 空域卷积.mp4 [356.8M]
┃ ┣━━3.1-3.2-3.3-3.4–L3空域图卷积介绍（一）.pdf [2.2M]
┃ ┣━━3.3-3.4 空域卷积.mp4 [132.9M]
┃ ┣━━3.5-3.6-v5.0过平滑现象.pdf [2.4M]
┃ ┣━━3.5图卷积网络回顾 空域图卷积2.mp4 [163.9M]
┃ ┗━━3.6过平滑现象.mp4 [310.2M]
┣━━04.第4章 图卷积的实践应用 [463.6M]
┃ ┣━━第五节课.pdf [3.1M]
┃ ┗━━图卷积神经网络的应用.mp4 [460.5M]
┣━━05.第5章 实践：基于PyG的图卷积的节点分类 [1.1G]
┃ ┣━━第1节 环境搭建 [336M]
┃ ┃ ┗━━【视频】环境搭建.mp4 [336M]
┃ ┣━━第2节 基于PyG框架的节点分类实践 [429.5M]
┃ ┃ ┣━━16：【视频】节点分类实践（上）.mp4 [229.3M]
┃ ┃ ┗━━16：【视频】节点分类实践（下）.mp4 [200.2M]
┃ ┣━━第3节 构造自己的数据集&查阅其他GCN方法 [309.1M]
┃ ┃ ┗━━17：【视频】构造自己的数据集&查阅其他GCN方法.mp4 [309.1M]
┃ ┣━━第4节 实践作业 [141.1K]
┃ ┃ ┣━━第六次课.pdf [139K]
┃ ┃ ┗━━节点分类code.rar [2.1K]
┃ ┣━━19：第五章作业讲评.mp4 [61.5M]
┃ ┣━━保存模型与相关代码.zip [4.4M]
┃ ┗━━实践作业.pdf [279.8K]
┣━━06.第6章 实践：基于Pytorch的图卷积的交通预测 [840.9M]
┃ ┣━━第1节 课件&代码 [31.4M]
┃ ┃ ┣━━第七次课.pdf [259.7K]
┃ ┃ ┗━━code.rar [31.1M]
┃ ┣━━第2节 时序数据处理及建模 [349.4M]
┃ ┃ ┗━━20：【视频】时序数据处理及建模.mp4 [349.4M]
┃ ┣━━第3节 基于Pytorch的交通流量预测 [427.1M]
┃ ┃ ┗━━21：【视频】基于Pytorch的交通流量预测.mp4 [427.1M]
┃ ┣━━第4节 作业 [266.5K]
┃ ┃ ┗━━作业.pdf [266.5K]
┃ ┗━━图卷积第6章优秀作业（PCCH）.zip [32.8M]
┣━━图神经网络（GNN）100篇论文集 [307.8M]
┃ ┣━━Applications [201.8M]
┃ ┃ ┣━━combinatorial optimization [3.4M]
┃ ┃ ┃ ┣━━Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search(1).pdf [537K]
┃ ┃ ┃ ┗━━Learning Combinatorial Optimization Algorithms over Graphs.pdf [2.9M]
┃ ┃ ┣━━graph generation [3.3M]
┃ ┃ ┃ ┣━━Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation.pdf [518K]
┃ ┃ ┃ ┣━━MolGAN- An implicit generative model for small molecular graphs(1).pdf [1.1M]
┃ ┃ ┃ ┗━━NetGAN- Generating Graphs via Random Walks(1).pdf [1.7M]
┃ ┃ ┣━━image [40.4M]
┃ ┃ ┃ ┣━━Image classification [1.7M]
┃ ┃ ┃ ┃ ┗━━Few-Shot Learning with Graph Neural Networks.pdf [1.7M]
┃ ┃ ┃ ┣━━Interaction Detection [1.1M]
┃ ┃ ┃ ┃ ┗━━Structural-RNN- Deep Learning on Spatio-Temporal Graphs.pdf [1.1M]
┃ ┃ ┃ ┣━━Object Detection [2.6M]
┃ ┃ ┃ ┃ ┣━━Learning Region features for Object Detection.pdf [1.7M]
┃ ┃ ┃ ┃ ┗━━Relation Networks for Object Detection.pdf [906.7K]
┃ ┃ ┃ ┣━━Region Classification [3.9M]
┃ ┃ ┃ ┃ ┗━━Iterative Visual Reasoning Beyond Convolutions..pdf [3.9M]
┃ ┃ ┃ ┣━━Semantic Segmentation [25M]
┃ ┃ ┃ ┃ ┣━━3D Graph Neural Networks for RGBD Semantic Segmentation.pdf [2.2M]
┃ ┃ ┃ ┃ ┣━━Dynamic Graph CNN for Learning on Point Clouds.pdf [5.1M]
┃ ┃ ┃ ┃ ┣━━Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs.pdf [4.8M]
┃ ┃ ┃ ┃ ┣━━Modeling polypharmacy side effects with graph convolutional networks.pdf [4.2M]
┃ ┃ ┃ ┃ ┗━━PointNet- Deep Learning on Point Sets for 3D Classification and Segmentation.pdf [8.7M]
┃ ┃ ┃ ┣━━Social Relationship Understanding [0B]
┃ ┃ ┃ ┗━━Visual Question Answering [6.2M]
┃ ┃ ┃ ┣━━Graph-Structured Representations for Visual Question Answering.pdf [3.7M]
┃ ┃ ┃ ┗━━Out of the Box- Reasoning with Graph Convolution Nets for Factual Visual Question Answering(1).pdf [2.4M]
┃ ┃ ┣━━knowledge graph [17.3M]
┃ ┃ ┃ ┣━━Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks.pdf [432.6K]
┃ ┃ ┃ ┣━━Deep Reasoning with Knowledge Graph for Social Relationship Understanding.pdf [2.8M]
┃ ┃ ┃ ┣━━Dynamic Graph Generation Network- Generating Relational Knowledge from Diagrams.pdf [1.2M]
┃ ┃ ┃ ┣━━Knowledge Transfer for Out-of-Knowledge-Base Entities – A Graph Neural Network Approach.pdf [355.2K]
┃ ┃ ┃ ┣━━Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering.pdf [437.8K]
┃ ┃ ┃ ┣━━Multi-Label Zero-Shot Learning with Structured Knowledge Graphs.pdf [1.4M]
┃ ┃ ┃ ┣━━Representation learning for visual-relational knowledge graphs.pdf [6.9M]
┃ ┃ ┃ ┣━━The More You Know- Using Knowledge Graphs for Image Classification.pdf [2.3M]
┃ ┃ ┃ ┗━━Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs.pdf [1.6M]
┃ ┃ ┣━━science [130.6M]
┃ ┃ ┃ ┣━━A Compositional Object-Based Approach to Learning Physical Dynamics.pdf [4.3M]
┃ ┃ ┃ ┣━━A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks.pdf [340.4K]
┃ ┃ ┃ ┣━━A simple neural network module for relational reasoning.pdf [1.4M]
┃ ┃ ┃ ┣━━Action Schema Networks- Generalised Policies with Deep Learning.pdf [1.7M]
┃ ┃ ┃ ┣━━Adversarial Attack on Graph Structured Data.pdf [593.1K]
┃ ┃ ┃ ┣━━Attend, Infer, Repeat- Fast Scene Understanding with Generative Models.pdf [1.3M]
┃ ┃ ┃ ┣━━Attention, Learn to Solve Routing Problems!.pdf [1.5M]
┃ ┃ ┃ ┣━━Beyond Categories- The Visual Memex Model for Reasoning About Object Relationships.pdf [618.7K]
┃ ┃ ┃ ┣━━Combining Neural Networks with Personalized PageRank for Classification on Graphs.pdf [483.2K]
┃ ┃ ┃ ┣━━Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders.pdf [567.1K]
┃ ┃ ┃ ┣━━Constructing Narrative Event Evolutionary Graph for Script Event Prediction.pdf [654.9K]
┃ ┃ ┃ ┣━━Conversation Modeling on Reddit using a Graph-Structured LSTM.pdf [682.3K]
┃ ┃ ┃ ┣━━Convolutional networks on graphs for learning molecular fingerprints.pdf [785.4K]
┃ ┃ ┃ ┣━━Cross-Sentence N-ary Relation Extraction with Graph LSTMs.pdf [540.9K]
┃ ┃ ┃ ┣━━Deep Graph Infomax.pdf [8.1M]
┃ ┃ ┃ ┣━━DeepInf- Modeling influence locality in large social networks.pdf [1.1M]
┃ ┃ ┃ ┣━━Discovering objects and their relations from entangled scene representations.pdf [5M]
┃ ┃ ┃ ┣━━Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs.pdf [567.1K]
┃ ┃ ┃ ┣━━Effective Approaches to Attention-based Neural Machine Translation.pdf [244K]
┃ ┃ ┃ ┣━━Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks.pdf [7M]
┃ ┃ ┃ ┣━━Graph Convolutional Matrix Completion.pdf [733K]
┃ ┃ ┃ ┣━━Graph Convolutional Neural Networks for Web-Scale Recommender Systems.pdf [9.8M]
┃ ┃ ┃ ┣━━Graph networks as learnable physics engines for inference and control.pdf [2.7M]
┃ ┃ ┃ ┣━━GraphRNN- Generating Realistic Graphs with Deep Auto-regressive Models.pdf [2.4M]
┃ ┃ ┃ ┣━━Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification.pdf [2.5M]
┃ ┃ ┃ ┣━━Hyperbolic Attention Networks.pdf [3.1M]
┃ ┃ ┃ ┣━━Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks.pdf [304.2K]
┃ ┃ ┃ ┣━━Inference in Probabilistic Graphical Models by Graph Neural Networks.pdf [3.1M]
┃ ┃ ┃ ┣━━Interaction Networks for Learning about Objects, Relations and Physics.pdf [1.9M]
┃ ┃ ┃ ┣━━Learning a SAT Solver from Single-Bit Supervision.pdf [1.9M]
┃ ┃ ┃ ┣━━Learning Conditioned Graph Structures for Interpretable Visual Question Answering.pdf [8.5M]
┃ ┃ ┃ ┣━━Learning Deep Generative Models of Graphs.pdf [2.3M]
┃ ┃ ┃ ┣━━Learning Graphical State Transitions.pdf [1.5M]
┃ ┃ ┃ ┣━━Learning Human-Object Interactions by Graph Parsing Neural Networks.pdf [3.9M]
┃ ┃ ┃ ┣━━Learning model-based planning from scratch.pdf [1.3M]
┃ ┃ ┃ ┣━━Learning Multiagent Communication with Backpropagation.pdf [4.1M]
┃ ┃ ┃ ┣━━Learning to Represent Programs with Graphs.pdf [421.9K]
┃ ┃ ┃ ┣━━Metacontrol for Adaptive Imagination-Based Optimization.pdf [1.6M]
┃ ┃ ┃ ┣━━Molecular Graph Convolutions- Moving Beyond Fingerprints.pdf [2.1M]
┃ ┃ ┃ ┣━━NerveNet Learning Structured Policy with Graph Neural Networks.pdf [3.1M]
┃ ┃ ┃ ┣━━Neural Combinatorial Optimization with Reinforcement Learning.pdf [393.2K]
┃ ┃ ┃ ┣━━Neural Module Networks.pdf [1M]
┃ ┃ ┃ ┣━━Neural Relational Inference for Interacting Systems.pdf [2.8M]
┃ ┃ ┃ ┣━━Protein Interface Prediction using Graph Convolutional Networks.pdf [837.8K]
┃ ┃ ┃ ┣━━Relational Deep Reinforcement Learning.pdf [6.8M]
┃ ┃ ┃ ┣━━Relational inductive bias for physical construction in humans and machines.pdf [1022.5K]
┃ ┃ ┃ ┣━━Relational neural expectation maximization- Unsupervised discovery of objects and their interactions.pdf [1.2M]
┃ ┃ ┃ ┣━━Self-Attention with Relative Position Representations.pdf [229.9K]
┃ ┃ ┃ ┣━━Semi-supervised User Geolocation via Graph Convolutional Networks.pdf [1.1M]
┃ ┃ ┃ ┣━━Situation Recognition with Graph Neural Networks.pdf [5.3M]
┃ ┃ ┃ ┣━━Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition.pdf [1.5M]
┃ ┃ ┃ ┣━━Spatio-Temporal Graph Convolutional Networks- A Deep Learning Framework for Traffic Forecasting.pdf [895K]
┃ ┃ ┃ ┣━━Structured Dialogue Policy with Graph Neural Networks.pdf [779.2K]
┃ ┃ ┃ ┣━━Symbolic Graph Reasoning Meets Convolutions.pdf [3.2M]
┃ ┃ ┃ ┣━━Traffic Graph Convolutional Recurrent Neural Network- A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting.pdf [1.5M]
┃ ┃ ┃ ┣━━Translating Embeddings for Modeling Multi-relational Data.pdf [414.2K]
┃ ┃ ┃ ┣━━Understanding Kin Relationships in a Photo.pdf [1.4M]
┃ ┃ ┃ ┣━━VAIN- Attentional Multi-agent Predictive Modeling.pdf [424K]
┃ ┃ ┃ ┗━━Visual Interaction Networks- Learning a Physics Simulator from Vide.o.pdf [5.4M]
┃ ┃ ┗━━text [6.7M]
┃ ┃ ┣━━Sequence Labeling [0B]
┃ ┃ ┣━━Text classification [0B]
┃ ┃ ┣━━A Graph-to-Sequence Model for AMR-to-Text Generation.pdf [290.2K]
┃ ┃ ┣━━Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling.pdf [621.9K]
┃ ┃ ┣━━End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures.pdf [363.1K]
┃ ┃ ┣━━Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks.pdf [604.6K]
┃ ┃ ┣━━Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks..pdf [453.5K]
┃ ┃ ┣━━Graph Convolution over Pruned Dependency Trees Improves Relation Extraction.pdf [784.4K]
┃ ┃ ┣━━Graph Convolutional Encoders for Syntax-aware Neural Machine Translation.pdf [346.9K]
┃ ┃ ┣━━Graph Convolutional Networks for Text Classification.pdf [1.8M]
┃ ┃ ┣━━Graph Convolutional Networks with Argument-Aware Pooling for Event Detection.pdf [324.7K]
┃ ┃ ┣━━Jointly Multiple Events Extraction via Attention-based Graph.pdf [430.4K]
┃ ┃ ┣━━N-ary relation extraction using graph state LSTM.pdf [455.7K]
┃ ┃ ┗━━Recurrent Relational Networks.pdf [307K]
┃ ┣━━Models [81M]
┃ ┃ ┣━━graph_type [16.1M]
┃ ┃ ┃ ┣━━directed graph [4.2M]
┃ ┃ ┃ ┃ ┗━━Rethinking Knowledge Graph Propagation for Zero-Shot Learning.pdf [4.2M]
┃ ┃ ┃ ┣━━edge-informative graph [4.4M]
┃ ┃ ┃ ┃ ┣━━Graph-to-Sequence Learning using Gated Graph Neural Networks.pdf [4.1M]
┃ ┃ ┃ ┃ ┗━━Modeling relational data with graph convolutional networks.pdf [323.6K]
┃ ┃ ┃ ┣━━heterogeneous graphs [0B]
┃ ┃ ┃ ┣━━Adaptive Graph Convolutional Neural Networks.pdf [803.9K]
┃ ┃ ┃ ┣━━Graph Capsule Convolutional Neural Networks.pdf [1.9M]
┃ ┃ ┃ ┣━━Graph Neural Networks for Object Localization.pdf [221.8K]
┃ ┃ ┃ ┣━━Graph Neural Networks for Ranking Web Pages.pdf [1M]
┃ ┃ ┃ ┣━━Graph Partition Neural Networks for Semi-Supervised Classification.pdf [713.9K]
┃ ┃ ┃ ┣━━How Powerful are Graph Neural Networks-.pdf [678.3K]
┃ ┃ ┃ ┣━━Mean-field theory of graph neural networks in graph partitioning.pdf [369.4K]
┃ ┃ ┃ ┗━━Spectral Networks and Locally Connected Networks on Graphs.pdf [1.9M]
┃ ┃ ┣━━others [30.7M]
┃ ┃ ┃ ┣━━A Comparison between Recursive Neural Networks and Graph Neural Networks.pdf [247.2K]
┃ ┃ ┃ ┣━━A new model for learning in graph domains.pdf [177.6K]
┃ ┃ ┃ ┣━━CelebrityNet- A Social Network Constructed from Large-Scale Online Celebrity Images.pdf [16.3M]
┃ ┃ ┃ ┣━━Contextual Graph Markov Model- A Deep and Generative Approach to Graph Processing.pdf [570.6K]
┃ ┃ ┃ ┣━━Deep Sets.pdf [5.1M]
┃ ┃ ┃ ┣━━Deriving Neural Architectures from Sequence and Graph Kernels.pdf [687K]
┃ ┃ ┃ ┣━━Diffusion-Convolutional Neural Networks.pdf [366.4K]
┃ ┃ ┃ ┗━━Geometric deep learning on graphs and manifolds using mixture model cnns.pdf [7.2M]
┃ ┃ ┣━━propagation_type [20.7M]
┃ ┃ ┃ ┣━━attention [6.1M]
┃ ┃ ┃ ┃ ┣━━Attention Is All You Need.pdf [2.1M]
┃ ┃ ┃ ┃ ┣━━Graph Attention Networks.pdf [1.5M]
┃ ┃ ┃ ┃ ┗━━Graph Classification using Structural Attention.pdf [2.5M]
┃ ┃ ┃ ┣━━convolution [9.5M]
┃ ┃ ┃ ┃ ┣━━Bayesian Semi-supervised Learning with Graph Gaussian Processes.pdf [689.9K]
┃ ┃ ┃ ┃ ┣━━Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering.pdf [459.4K]
┃ ┃ ┃ ┃ ┣━━Deep Convolutional Networks on Graph-Structured Data.pdf [4.6M]
┃ ┃ ┃ ┃ ┣━━Learning Convolutional Neural Networks for Graphs.pdf [639.9K]
┃ ┃ ┃ ┃ ┣━━Spectral Networks and Deep Locally Connected.pdf [1.9M]
┃ ┃ ┃ ┃ ┗━━Structure-Aware Convolutional Neural Networks.pdf [1.4M]
┃ ┃ ┃ ┣━━gate [1.2M]
┃ ┃ ┃ ┃ ┣━━Gated Graph Sequence Neural Networks.pdf [748.2K]
┃ ┃ ┃ ┃ ┗━━Sentence-State LSTM for Text Representation.pdf [442.3K]
┃ ┃ ┃ ┗━━skip [4M]
┃ ┃ ┃ ┣━━Representation Learning on Graphs with Jumping Knowledge Networks.pdf [3.2M]
┃ ┃ ┃ ┗━━Semi-Supervised Classification with Graph Convolutional Networks.pdf [853.4K]
┃ ┃ ┗━━training methods [13.5M]
┃ ┃ ┣━━boosting [2M]
┃ ┃ ┃ ┗━━Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning.pdf [2M]
┃ ┃ ┣━━neighborhood sampling [2M]
┃ ┃ ┃ ┣━━Adaptive Sampling Towards Fast Graph Representation Learning.pdf [580K]
┃ ┃ ┃ ┣━━FastGCN- Fast Learning with Graph Convolutional Networks via Importance Sampling.pdf [358.3K]
┃ ┃ ┃ ┗━━Inductive Representation Learning on Large Graphs.pdf [1M]
┃ ┃ ┣━━receptive field control [1.2M]
┃ ┃ ┃ ┗━━Stochastic Training of Graph Convolutional Networks with Variance Reduction.pdf [1.2M]
┃ ┃ ┣━━Covariant Compositional Networks For Learning Graphs.pdf [482.5K]
┃ ┃ ┣━━Graphical-Based Learning Environments for Pattern Recognition.pdf [335.9K]
┃ ┃ ┣━━Hierarchical Graph Representation Learning with Differentiable Pooling.pdf [2.3M]
┃ ┃ ┣━━Knowledge-Guided Recurrent Neural Network Learning for Task-Oriented Action Prediction.pdf [1000.5K]
┃ ┃ ┣━━Learning Steady-States of Iterative Algorithms over Graphs.pdf [3.1M]
┃ ┃ ┗━━Neural networks for relational learning- an experimental comparison.pdf [1.1M]
┃ ┗━━Survey [25M]
┃ ┣━━极力推荐 [14.3M]
┃ ┃ ┣━━Graph Neural Networks：A Review of Methods and Applications.pdf [2.7M]
┃ ┃ ┣━━Non-local Neural Networks.pdf [1.2M]
┃ ┃ ┣━━Relational Inductive Biases, Deep Learning, and Graph Networks.pdf [9M]
┃ ┃ ┗━━The Graph Neural Network Model.pdf [1.4M]
┃ ┗━━一般推荐 [10.6M]
┃ ┣━━A Comprehensive Survey on Graph Neural Networks.pdf [1.8M]
┃ ┣━━Computational Capabilities of Graph Neural Networks(1).pdf [1.3M]
┃ ┣━━Deep Learning on Graphs- A Survey.pdf [1.8M]
┃ ┣━━Geometric Deep Learning- Going beyond Euclidean data.pdf [5.3M]
┃ ┗━━Neural Message Passing for Quantum Chemistry.pdf [511.1K]
┗━━图卷积神经网络开课仪式.pptx [362.7K]

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