School project WIP. Implementation of the paper 'Neural Mesh Simplification' by Potamias et al.

Some useful gadgets that may help you progress through your preparation for the GRE test, or any English learning and exams. | 一些用于帮助你准备GRE考试，或任何英语考试、英语学习的小玩意

Exercises on Machine Learning

Code I used for my YouTube videos

Parallel Reverse Mode Automatic Differentiation in C# for Custom Neural Network Development

PyTorch implementation of GNN models

WholeGraph - large scale Graph Neural Networks

Evaluating the generalizability of graph neural networks for predicting collision cross section

DL based representation learning of MS imaging data

This repository is used to collect papers and code in the field of AI.

Official implement of RAHG: A Role-Aware Hypergraph Neural Network for Node Classification in Graphs.

Holistic understanding of Large Language Models (LLMs) involves integrating NLP, computer vision, audio processing, and reinforcement learning. GNNs capture intricate data relationships. Attention mechanisms, Transformer architectures, vision-language pre-training, audio processing with spectrograms, pre-trained embeddings, and reinforcement .

書籍『グラフニューラルネットワーク』のサポートサイトです。

Implement, test, and organize recent reseach of GNN-based methods. Enable lifecycle controlled with MLflow.

This repository is the implementation of the paper Semi-Supervised Classification With Graph Convolutional Networks (aka GCN) by Kipf et al., ICLR 2017.

[ICML'24W] Revisiting Random Walks for Learning on Graphs, in PyTorch

Machine learning on graphs

A GNN-based surrogate model of urban drainage networks.

Reconstruct billions of particle trajectories with graph neural networks

GECCO is a lightweight image classifier based on single MLP and graph convolutional layers. We find that our model can achieve up to 16x better latency than other state-of-the-art models. The paper for our model can be found at https://arxiv.org/abs/2402.00564