Code I used for my YouTube videos
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Updated
Jun 20, 2024 - Jupyter Notebook
Code I used for my YouTube videos
Deep Learning Inference benchmark. Supports OpenVINO™ toolkit, Caffe, TensorFlow, TensorFlow Lite, ONNX Runtime, OpenCV DNN, MXNet, PyTorch, Apache TVM, ncnn, etc.
This is an open-source toolkit for Heterogeneous Graph Neural Network(OpenHGNN) based on DGL.
Protein Graph Library
Open MatSci ML Toolkit is a framework for prototyping and scaling out deep learning models for materials discovery supporting widely used materials science datasets, and built on top of PyTorch Lightning, the Deep Graph Library, and PyTorch Geometric.
Multilabel Aspect Prediction using Graph Convolutional Networks
This repository contains the official implementation of the paper titled Multimodal weighted graph representation for information extraction from visually rich documents.
An end-to-end blueprint architecture for real-time fraud detection(leveraging graph database Amazon Neptune) using Amazon SageMaker and Deep Graph Library (DGL) to construct a heterogeneous graph from tabular data and train a Graph Neural Network(GNN) model to detect fraudulent transactions in the IEEE-CIS dataset.
NebulaGraph DGL(Deep Graph Library) Integration Package. (WIP)
[CIKM 2023] GUARD: Graph Universal Adversarial Defense
Collab implementation for Fraud Detection in Graph Neural Networks, based on Deep Graph Library (DGL) and PyTorch backend. About Colab implementation for Fraud Detection in Graph Neural Networks, based on Deep Graph Library (DGL) and PyTorch backend.
Official DGL Implementation of "GraphSAINT-NRW, ERW: Subgraph Sampling Techniques using Random Walk Reflecting Node Degree". KSC 2022
DGL Implementation of "Efficient Sampling Techniques for Embedding Large Graphs". KCC 2022
Official DGL Implementation of "Distributed Graph Data Augmentation Technique for Graph Neural Network". KSC 2023
This Repository includes DGL tutorials and various information related to graph neural networks.
This repository stores the code implemented to generate the results of our paper: Machine learning strategies to predict late adverse effects in childhood acute lymphoblastic leukemia survivors
High performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings.
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