#计算机科学#Graph Neural Network Library for PyTorch
翻译 - PyTorch的几何深度学习扩展库
#计算机科学#Python package built to ease deep learning on graph, on top of existing DL frameworks.
翻译 - 在现有DL框架之上构建的Python软件包,用于简化图上的深度学习。
#自然语言处理#Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
翻译 - 通过从这些令人兴奋的演讲中学习,可以深入学习深度学习,强化学习,机器学习,计算机视觉和自然语言学习!
#Awesome#Anomaly detection related books, papers, videos, and toolboxes
翻译 - 与异常检测相关的书籍,论文,视频和工具箱
#计算机科学#links to conference publications in graph-based deep learning
翻译 - 基于图的深度学习中会议出版物的链接
#计算机科学#A unified, comprehensive and efficient recommendation library
翻译 - 统一,全面,高效的推荐库
#计算机科学#SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)
翻译 - SuperGlue:学习功能与图神经网络的匹配(CVPR 2020,口腔)
🔨 🍇 💻 🚀 GraphScope: A One-Stop Large-Scale Graph Computing System from Alibaba | 一站式图计算系统
#计算机科学#精选机器学习,NLP,图像识别, 深度学习等人工智能领域学习资料,搜索,推荐,广告系统架构及算法技术资料整理。算法大牛笔记汇总
#计算机科学#StellarGraph - Machine Learning on Graphs
翻译 - StellarGraph-图上的机器学习
#计算机科学#A distributed graph deep learning framework.
翻译 - 分布式图深度学习框架。
#计算机科学#PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
翻译 - PyTorch Geometric的时间扩展库
#计算机科学#Repository for benchmarking graph neural networks
翻译 - 基准图神经网络的存储库
#计算机科学#Graph Neural Networks with Keras and Tensorflow 2.
翻译 - 使用Keras和Tensorflow的图神经网络2。
#数据仓库#Benchmark datasets, data loaders, and evaluators for graph machine learning
#自然语言处理#😎 An up-to-date & curated list of awesome semi-supervised learning papers, methods & resources.
CogDL: A Comprehensive Library for Graph Deep Learning (WWW 2023)
翻译 - CogDL:用于图形的广泛研究工具包
#自然语言处理#Graph4nlp is the library for the easy use of Graph Neural Networks for NLP. Welcome to visit our DLG4NLP website (https://dlg4nlp.github.io/index.html) for various learning resources!
翻译 - Graph4nlp 是一个用于轻松使用 NLP 的图神经网络的库
#计算机科学#Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).
#自然语言处理#Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)