This is the source code for: Context-aware Entity Typing in Knowledge Graphs.

Related tags

Deep LearningCET
Overview

Context-aware Entity Typing in Knowledge Graphs

This is the source code for: Context-aware Entity Typing in Knowledge Graphs.

Requirements

  • Python 3
  • PyTorch >= 1.6.0
  • dgl >= 0.5.3

Usage

Data preprocessing:

cd data
python preprocess.py --dataset FB15kET
python preprocess.py --dataset YAGO43kET

Train:

########### FB15kET ###########
# CET
python run.py --model CET --dataset FB15kET --load_ET --load_KG --neighbor_sampling \
--hidden_dim 100 --temperature 0.5 --lr 0.001 --loss FNA --beta 4.0 --cuda

# R-GCN
python run.py --model RGCN --dataset FB15kET --load_ET --load_KG --neighbor_sampling \
--hidden_dim 100 --lr 0.001 --loss FNA --beta 3.0 --cuda

# CompGCN
python run.py --model CompGCN --dataset FB15kET --load_ET --load_KG --neighbor_sampling \
--hidden_dim 100 --lr 0.001 --loss FNA --activation relu --cuda

########### YAGO43kET ###########
# CET
python run.py --model CET --dataset YAGO43kET --load_ET --load_KG --neighbor_sampling \
--hidden_dim 100 --temperature 0.5 --lr 0.001 --loss FNA --beta 2.0 --cuda

# R-GCN
python run.py --model RGCN --dataset YAGO43kET --load_ET --load_KG --neighbor_sampling \
--hidden_dim 100 --lr 0.001 --loss FNA --beta 2.0 --cuda

# CompGCN
python run.py --model CompGCN --dataset YAGO43kET --load_ET --load_KG --neighbor_sampling \
--hidden_dim 100 --lr 0.001 --loss FNA --activation relu --cuda

The KGE baselines can be found in KGE_baselines.

Acknowledgement

We refer to the code of DGL. Thanks for their contributions.

Official Repo for ICCV2021 Paper: Learning to Regress Bodies from Images using Differentiable Semantic Rendering

[ICCV2021] Learning to Regress Bodies from Images using Differentiable Semantic Rendering Getting Started DSR has been implemented and tested on Ubunt

Sai Kumar Dwivedi 83 Nov 27, 2022
Light-weight network, depth estimation, knowledge distillation, real-time depth estimation, auxiliary data.

light-weight-depth-estimation Boosting Light-Weight Depth Estimation Via Knowledge Distillation, https://arxiv.org/abs/2105.06143 Junjie Hu, Chenyou F

Junjie Hu 13 Dec 10, 2022
This toolkit provides codes to download and pre-process the SLUE datasets, train the baseline models, and evaluate SLUE tasks.

slue-toolkit We introduce Spoken Language Understanding Evaluation (SLUE) benchmark. This toolkit provides codes to download and pre-process the SLUE

ASAPP Research 39 Sep 21, 2022
This is a Keras implementation of a CNN for estimating age, gender and mask from a camera.

face-detector-age-gender This is a Keras implementation of a CNN for estimating age, gender and mask from a camera. Before run face detector app, expr

Devdreamsolution 2 Dec 04, 2021
Set of models for classifcation of 3D volumes

Classification models 3D Zoo - Keras and TF.Keras This repository contains 3D variants of popular CNN models for classification like ResNets, DenseNet

69 Dec 28, 2022
StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators

StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators [Project Website] [Replicate.ai Project] StyleGAN-NADA: CLIP-Guided Domain Adaptation

992 Dec 30, 2022
Code for 'Single Image 3D Shape Retrieval via Cross-Modal Instance and Category Contrastive Learning', ICCV 2021

CMIC-Retrieval Code for Single Image 3D Shape Retrieval via Cross-Modal Instance and Category Contrastive Learning. ICCV 2021. Introduction In this wo

42 Nov 17, 2022
FeTaQA: Free-form Table Question Answering

FeTaQA: Free-form Table Question Answering FeTaQA is a Free-form Table Question Answering dataset with 10K Wikipedia-based {table, question, free-form

Language, Information, and Learning at Yale 40 Dec 13, 2022
Code for the paper "Curriculum Dropout", ICCV 2017

Curriculum Dropout Dropout is a very effective way of regularizing neural networks. Stochastically "dropping out" units with a certain probability dis

Pietro Morerio 21 Jan 02, 2022
Official implementation for paper: A Latent Transformer for Disentangled Face Editing in Images and Videos.

A Latent Transformer for Disentangled Face Editing in Images and Videos Official implementation for paper: A Latent Transformer for Disentangled Face

InterDigital 108 Dec 09, 2022
A basic duplicate image detection service using perceptual image hash functions and nearest neighbor search, implemented using faiss, fastapi, and imagehash

Duplicate Image Detection Getting Started Install dependencies pip install -r requirements.txt Run service python main.py Testing Test with pytest How

Matthew Podolak 21 Nov 11, 2022
PyKaldi GOP-DNN on Epa-DB

PyKaldi GOP-DNN on Epa-DB This repository has the tools to run a PyKaldi GOP-DNN algorithm on Epa-DB, a database of non-native English speech by Spani

18 Dec 14, 2022
Learning cell communication from spatial graphs of cells

ncem Features Repository for the manuscript Fischer, D. S., Schaar, A. C. and Theis, F. Learning cell communication from spatial graphs of cells. 2021

Theis Lab 77 Dec 30, 2022
LaneAF: Robust Multi-Lane Detection with Affinity Fields

LaneAF: Robust Multi-Lane Detection with Affinity Fields This repository contains Pytorch code for training and testing LaneAF lane detection models i

155 Dec 17, 2022
Caffe-like explicit model constructor. C(onfig)Model

cmodel Caffe-like explicit model constructor. C(onfig)Model Installation pip install git+https://github.com/bonlime/cmodel Usage In order to allow usi

1 Feb 18, 2022
Benchmark datasets, data loaders, and evaluators for graph machine learning

Overview The Open Graph Benchmark (OGB) is a collection of benchmark datasets, data loaders, and evaluators for graph machine learning. Datasets cover

1.5k Jan 05, 2023
NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling @ INTERSPEECH 2021 Accepted

NU-Wave — Official PyTorch Implementation NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling Junhyeok Lee, Seungu Han @ MINDsLab Inc

MINDs Lab 242 Dec 23, 2022
Official codebase for "B-Pref: Benchmarking Preference-BasedReinforcement Learning" contains scripts to reproduce experiments.

B-Pref Official codebase for B-Pref: Benchmarking Preference-BasedReinforcement Learning contains scripts to reproduce experiments. Install conda env

48 Dec 20, 2022
In this project I played with mlflow, streamlit and fastapi to create a training and prediction app on digits

Fastapi + MLflow + streamlit Setup env. I hope I covered all. pip install -r requirements.txt Start app Go in the root dir and run these Streamlit str

76 Nov 23, 2022
The implementation of 'Image synthesis via semantic composition'.

Image synthesis via semantic synthesis [Project Page] by Yi Wang, Lu Qi, Ying-Cong Chen, Xiangyu Zhang, Jiaya Jia. Introduction This repository gives

DV Lab 71 Jan 06, 2023