Node Dependent Local Smoothing for Scalable Graph Learning

Last update: Nov 28, 2022

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Overview

Node Dependent Local Smoothing for Scalable Graph Learning

Requirements

Environments: Xeon Gold 5120 (CPU), 384GB(RAM), TITAN RTX (GPU), Ubuntu 16.04 (OS)

The PyTorch version we use is torch 1.7.1+cu110. Please refer to the official website -- https://pytorch.org/get-started/locally/ -- for the detailed installation instructions.

To install other requirements:

pip install -r requirements.txt

Training

To train the model(s) in the paper, run this command:

cd src; python train.py --dataset cora/citeseer/pubmed

Please refer to the Appendix for the detailed hyperparameters.

Node Classification Results

Transductive Setting:
Inductive Setting:
Efficiency Comparison:

Owner

Wentao Zhang

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GitHub Repository

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Node Dependent Local Smoothing for Scalable Graph Learning

Related tags

Overview

Node Dependent Local Smoothing for Scalable Graph Learning

Requirements

Training

Node Classification Results

Owner

Wentao Zhang

Source codes for Improved Few-Shot Visual Classification (CVPR 2020), Enhancing Few-Shot Image Classification with Unlabelled Examples

Official Implementation of Few-shot Visual Relationship Co-localization

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Exploring Machine Learning Models for detecting anomalous behavior in credit-card transactions. It's crucial that credit-card companies are able to recognize fraudulent activity so that customers are not charged for items they didn't purchase.

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Code for the prototype tool in our paper "CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning".

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View model summaries in PyTorch!

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Code for paper Novel View Synthesis via Depth-guided Skip Connections

An implementation of IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification

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Node Dependent Local Smoothing for Scalable Graph Learning

Related tags

Overview

Node Dependent Local Smoothing for Scalable Graph Learning

Requirements

Training

Node Classification Results

Owner

Wentao Zhang

Source codes for Improved Few-Shot Visual Classification (CVPR 2020), Enhancing Few-Shot Image Classification with Unlabelled Examples

Official Implementation of Few-shot Visual Relationship Co-localization

Collections for the lasted paper about multi-view clustering methods (papers, codes)

In this project, we create and implement a deep learning library from scratch.

This is a virtual picture dragging application. Users may virtually slide photos across the screen. The distance between the index and middle fingers determines the movement. Smaller distances indicate click and motion, whereas bigger distances indicate only hand movement.

Exploring Machine Learning Models for detecting anomalous behavior in credit-card transactions. It's crucial that credit-card companies are able to recognize fraudulent activity so that customers are not charged for items they didn't purchase.

Deep Learning Head Pose Estimation using PyTorch.

Code for the prototype tool in our paper "CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning".

Python scripts performing class agnostic object localization using the Object Localization Network model in ONNX.

View model summaries in PyTorch!

This repository contains the DendroMap implementation for scalable and interactive exploration of image datasets in machine learning.

Code for paper Novel View Synthesis via Depth-guided Skip Connections

An implementation of IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification

​TextWorld is a sandbox learning environment for the training and evaluation of reinforcement learning (RL) agents on text-based games.

Gluon CV Toolkit

Demonstrational Session git repo for H SAF User Workshop (28/1)

A pytorch-based real-time segmentation model for autonomous driving

The official code for PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization

A boosting-based Multiple Instance Learning (MIL) package that includes MIL-Boost and MCIL-Boost

VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning

TextWorld is a sandbox learning environment for the training and evaluation of reinforcement learning (RL) agents on text-based games.