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adversarial_attack-gnn_and_others

adversarial_training

Codebase used to generate the results for NeurIPS23 "Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions" paper.

alphatree-graphic-deep-neural-network

AI Roadmap:机器学习(Machine Learning)、深度学习(Deep Learning)、对抗神经网络(GAN），图神经网络（GNN），NLP，大数据相关的发展路书(roadmap), 并附海量源码（python，pytorch）带大家消化基本知识点，突破面试，完成从新手到合格工程师的跨越，其中深度学习相关论文附有tensorflow caffe官方源码，应用部分含推荐算法和知识图谱

awesome-ctf

A curated list of CTF frameworks, libraries, resources and softwares

awesome-llm-safety

A curated list of security-related papers, articles, and resources focused on Large Language Models (LLMs). This repository aims to provide researchers, practitioners, and enthusiasts with insights into the security implications, challenges, and advancements surrounding these powerful models.

awesome-self-supervised-gnn

Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).

awsome-front-end-learning-resource

:octocat:GitHub最全的前端资源汇总仓库（包括前端学习、开发资源、求职面试等）

chinese_license_plate_detection_recognition

yolov5 车牌检测 车牌识别 中文车牌识别 检测 支持12种中文车牌 支持双层车牌

gnnpapers

Must-read papers on graph neural networks (GNN)

graph-adversarial-learning

A curated collection of adversarial attack and defense on graph data.

graph-adversarial-learning-literature

A curated list of adversarial attacks and defenses papers on graph-structured data.

graph-based-deep-learning-literature

links to conference publications in graph-based deep learning

graph-fraud-detection-papers

A curated list of graph-based fraud, anomaly, and outlier detection papers & resources

graph-learning-reading-list

Advances on machine learning of graphs, covering the reading list of recent top academic conferences.

llm4graph

papers-of-robust-ml

Related papers for robust machine learning

pytorch_tutorial

《Pytorch模型训练实用教程》中配套代码

transferattack

TransferAttack is a pytorch framework to boost the adversarial transferability for image classification.

when-do-gnns-help

Official repository for NeurIPS 2023 paper "When Do Graph Neural Networks Help with Node Classification? Investigating the Impact of Homophily Principle on Node Distinguishability"