Showing 1-20 of 39 projects
Graph Neural Network library for PyTorch
A Python package for deep learning on graphs, built to ease existing DL frameworks.
A comprehensive collection of deep learning, reinforcement learning, and machine learning resources for vibe coders.
A comprehensive collection of resources for anomaly detection, including books, papers, videos, and toolboxes.
A unified, comprehensive and efficient recommendation library for building recommendation systems.
A PyTorch implementation of the SuperGlue graph neural network for feature matching and pose estimation.
A curated collection of AI and machine learning learning resources and tutorials for vibe coders
A PyTorch library for processing spatiotemporal graph data using neural machine learning models.
A distributed graph deep learning framework for building graph-based AI models and applications.
High-performance vector graph neural network database in Rust for real-time AI inference and graph ML.
A library for building graph neural networks using Keras and TensorFlow 2.
A Python library for graph deep learning research, focused on 3D graphs, explainable ML, and self-supervised learning.
Comprehensive collection of federated learning resources (papers, frameworks, datasets, tutorials, etc.)
This project provides a code repository for the book "Dive into Graph Neural Networks: Understanding GNN Principles".
A curated list of awesome papers, methods, and resources for semi-supervised learning, a powerful machine learning technique.
CogDL is a comprehensive library for graph deep learning, providing state-of-the-art models and utilities for graph-based tasks.
A curated list of Graph/Transformer-based papers and resources for fraud, anomaly, and outlier detection.
A repository of papers on pretraining and self-supervised learning for graph neural networks.
Graph4nlp is a library for easy use of Graph Neural Networks for Natural Language Processing (NLP) tasks.
An efficient and flexible graph learning framework based on PaddlePaddle, with support for heterogeneous graphs and meta-path learning.
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