Code for paper PairRE: Knowledge Graph Embeddings via Paired Relation Vectors.

Overview

PairRE

Code for paper PairRE: Knowledge Graph Embeddings via Paired Relation Vectors.

This implementation of PairRE for Open Graph Benchmak datasets (ogbl-wikikg and ogbl-biokg) is based on OGB. Thanks for their contributions.

Setup

To run the code, you need the following dependencies:

Results

The results of PairRE on ogbl-wikikg, ogbl-biokg and ogbl-wikikg2 are as follows.

ogbl-wikikg

#Dim #Parameters Hardware Test MRR Valid MRR
PairRE 100 250,167,400 16GB GPU 0.4912 ± 0.004 0.5013 ± 0.004
PairRE 200 500,334,800 16GB GPU 0.5289 ± 0.003 0.5529 ± 0.001

ogbl-biokg

#Dim #Parameters Hardware Test MRR Valid MRR
PairRE 2000 187,750,000 16GB GPU 0.8164 ± 0.0005 0.8172 ± 0.0005

ogbl-wikikg2

#Dim #Parameters Hardware Test MRR Valid MRR
PairRE 100 250,167,400 16GB GPU 0.4849 ± 0.003 0.4941 ± 0.004
PairRE 200 500,334,800 16GB GPU 0.5208 ± 0.003 0.5423 ± 0.002

Running the code

ogbl-wikikg

cd wikikg && sh examples.sh

ogbl-biokg

cd biokg && sh examples.sh

ogbl-wikikg2

Please update ogb package to version 1.2.4. The hyperparameters are same to the experiments in ogbl-wikikg.

cd wikikg && sh examples.sh

The details of the optional hyperparameters can be found in examples.sh.

Owner
Alipay
Ant Group Open Source
Alipay
PyTorch implementation of Decoupling Value and Policy for Generalization in Reinforcement Learning

PyTorch implementation of Decoupling Value and Policy for Generalization in Reinforcement Learning

48 Dec 08, 2022
A Python module for the generation and training of an entry-level feedforward neural network.

ff-neural-network A Python module for the generation and training of an entry-level feedforward neural network. This repository serves as a repurposin

Riadh 2 Jan 31, 2022
Chess reinforcement learning by AlphaGo Zero methods.

About Chess reinforcement learning by AlphaGo Zero methods. This project is based on these main resources: DeepMind's Oct 19th publication: Mastering

Samuel 2k Dec 29, 2022
This repo is to be freely used by ML devs to check the GAN performances without coding from scratch.

GANs for Fun Created because I can! GOAL The goal of this repo is to be freely used by ML devs to check the GAN performances without coding from scrat

Sagnik Roy 13 Jan 26, 2022
Implementing DropPath/StochasticDepth in PyTorch

%load_ext memory_profiler Implementing Stochastic Depth/Drop Path In PyTorch DropPath is available on glasses my computer vision library! Introduction

Francesco Saverio Zuppichini 13 Jan 05, 2023
The official implementation for "FQ-ViT: Fully Quantized Vision Transformer without Retraining".

FQ-ViT [arXiv] This repo contains the official implementation of "FQ-ViT: Fully Quantized Vision Transformer without Retraining". Table of Contents In

132 Jan 08, 2023
MarcoPolo is a clustering-free approach to the exploration of bimodally expressed genes along with group information in single-cell RNA-seq data

MarcoPolo is a method to discover differentially expressed genes in single-cell RNA-seq data without depending on prior clustering Overview MarcoPolo

Chanwoo Kim 13 Dec 18, 2022
The missing CMake project initializer

cmake-init - The missing CMake project initializer Opinionated CMake project initializer to generate CMake projects that are FetchContent ready, separ

1k Jan 01, 2023
Line-level Handwritten Text Recognition (HTR) system implemented with TensorFlow.

Line-level Handwritten Text Recognition with TensorFlow This model is an extended version of the Simple HTR system implemented by @Harald Scheidl and

Hoàng Tùng Lâm (Linus) 72 May 07, 2022
Unicorn can be used for performance analyses of highly configurable systems with causal reasoning

Unicorn can be used for performance analyses of highly configurable systems with causal reasoning. Users or developers can query Unicorn for a performance task.

AISys Lab 27 Jan 05, 2023
RARA: Zero-shot Sim2Real Visual Navigation with Following Foreground Cues

RARA: Zero-shot Sim2Real Visual Navigation with Following Foreground Cues FGBG (foreground-background) pytorch package for defining and training model

Klaas Kelchtermans 1 Jun 02, 2022
SC-GlowTTS: an Efficient Zero-Shot Multi-Speaker Text-To-Speech Model

SC-GlowTTS: an Efficient Zero-Shot Multi-Speaker Text-To-Speech Model Edresson Casanova, Christopher Shulby, Eren Gölge, Nicolas Michael Müller, Frede

Edresson Casanova 92 Dec 09, 2022
PyTorch implementation of ARM-Net: Adaptive Relation Modeling Network for Structured Data.

A ready-to-use framework of latest models for structured (tabular) data learning with PyTorch. Applications include recommendation, CRT prediction, healthcare analytics, and etc.

48 Nov 30, 2022
Deep learning with TensorFlow and earth observation data.

Deep Learning with TensorFlow and EO Data Complete file set for Jupyter Book Autor: Development Seed Date: 04 October 2021 ISBN: (to come) Notebook tu

Development Seed 20 Nov 16, 2022
Official PyTorch code for WACV 2022 paper "CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows"

CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows WACV 2022 preprint:https://arxiv.org/abs/2107.1

Denis 156 Dec 28, 2022
Code of Classification Saliency-Based Rule for Visible and Infrared Image Fusion

CSF Code of Classification Saliency-Based Rule for Visible and Infrared Image Fusion Tips: For testing: CUDA_VISIBLE_DEVICES=0 python main.py For trai

Han Xu 14 Oct 31, 2022
This repository comes with the paper "On the Robustness of Counterfactual Explanations to Adverse Perturbations"

Robust Counterfactual Explanations This repository comes with the paper "On the Robustness of Counterfactual Explanations to Adverse Perturbations". I

Marco 5 Dec 20, 2022
Solution of Kaggle competition: Sartorius - Cell Instance Segmentation

Sartorius - Cell Instance Segmentation https://www.kaggle.com/c/sartorius-cell-instance-segmentation Environment setup Build docker image bash .dev_sc

68 Dec 09, 2022
This repository provides a basic implementation of our GCPR 2021 paper "Learning Conditional Invariance through Cycle Consistency"

Learning Conditional Invariance through Cycle Consistency This repository provides a basic TensorFlow 1 implementation of the proposed model in our GC

BMDA - University of Basel 1 Nov 04, 2022
Mae segmentation - Reproduction of semantic segmentation using masked autoencoder (mae)

ADE20k Semantic segmentation with MAE Getting started Install the mmsegmentation

97 Dec 17, 2022