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Name: Yanwei Yu
Type: User
Name: Yanwei Yu
Type: User
Co-embedding Attributed Networks
conditional variational autoencoder written in Keras
This project reproduces the book Dive Into Deep Learning (www.d2l.ai), adapting the code from MXNet into PyTorch.
《动手学深度学习》:面向中文读者、能运行、可讨论。英文版即伯克利“深度学习导论”教材。
本仓库将PyTorch官方书籍《Deep learning with PyTorch》(基本摘录版)翻译成中文版并给出可运行的相关代码。
Slides for the teaching and learning about deep learning
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。
Dynamic graph/network dataset for dynamic graph/network embedding/representation
FAME source code
Traffic Graph Convolutional Recurrent Neural Network
Graph-based LSTM for taxi demand prediction
Collection of autoencoders written in Keras
Keras implementation of LSTM Variational Autoencoder
Tensorflow Implementation of Large-Scale Learnable Graph Convolutional Networks (LGCN) KDD18
A roadmap connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.
KDD 2022 Multiplex Heterogeneous Graph Convolutional Network
Taxi Demand prediction using text processing for different zones in brooklyn, New York.
New York Taxi trip demand prediction using Machine learning models trained with temporal features
This is an open-source toolkit for Heterogeneous Graph Neural Network(OpenHGNN) based on DGL.
A combination of Autoencoder and Robust PCA
SUMO is an open source, highly portable, microscopic and continuous road traffic simulation package designed to handle large road networks. It allows for intermodal simulation including pedestrians and comes with a large set of tools for scenario creation.
Temporal Graph Convolutional Network for Urban Traffic Flow Prediction Method
Variational AutoEncoder - Keras implementation on mnist and cifar10 datasets
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