Y. Zhang, Q. Yao, W. Dai, L. Chen. AutoSF: Searching Scoring Functions for Knowledge Graph Embedding. IEEE International Conference on Data Engineering (ICDE). 2020

Overview

AutoSF

The code for our paper "AutoSF: Searching Scoring Functions for Knowledge Graph Embedding" and this paper has been accepted by ICDE2020.

News: (2021.4) AutoSF-OGB for Open Graph Benchmark is released.

Readers are welcomed to fork this repository to reproduce the experiments and follow our work. Please kindly cite our paper

@inproceedings{zhang2019autosf,
  title={AutoSF: Searching Scoring Functions for Knowledge Graph Embedding},
  author={Zhang, Yongqi and Yao, Quanming and Dai, Wenyuan and Chen, Lei},
  booktitle={2020 IEEE 36th International Conference on Data Engineering (ICDE)},
  pages={433--444},
  year={2020},
  organization={IEEE}
}

Instructions

For the sake of ease, a quick instruction is given for readers to reproduce the whole process. Note that the programs are tested on Linux(Ubuntu release 16.04), Python 3.7 from Anaconda 4.5.11.

Install PyTorch (>0.4.0)

conda install pytorch -c pytorch

Get this repo

git clone https://github.com/yzhangee/AutoSF
cd AutoSF
tar -zvxf KG_Data.tar.gz 

Reproducing the searching/fine-tuning/evaluation procedure, please refer to the bash file "run.sh"

bash run.sh

Explaination of the searched SFs in the file "searched_SFs.txt":

  • The first 4 values (a,b,c,d) represent h_1 * r_1 * t_a + h_2 * r_2 * t_b + h_3 * r_3 * t_c + h_4 * r_4 * t_d.

  • For the others, every 4 values represent one adding block: index of r, index of h, index of t, the sign s.

You can also rely on the "evaluate.py" file to evaluate the searched SFs by manually setting the struct variable.

Related AutoML papers (ML Research group in 4Paradigm)

  • Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding. NeurIPS 2020 papercode
  • Efficient Neural Interaction Functions Search for Collaborative Filtering. WWW 2020 paper code
  • Efficient Neural Architecture Search via Proximal Iterations. AAAI 2020. paper code
  • Simple and Automated Negative Sampling for Knowledge Graph Embedding. ICDE 2019 paper code
  • Taking Human out of Learning Applications: A Survey on Automated Machine Learning. Arxiv 2018 paper
Owner
AutoML Research
A compact machine learning research group focusing on automated machine learning (AutoML), meta-learning and neural architecture search (NAS).
AutoML Research
Jupyter Dock is a set of Jupyter Notebooks for performing molecular docking protocols interactively, as well as visualizing, converting file formats and analyzing the results.

Molecular Docking integrated in Jupyter Notebooks Description | Citation | Installation | Examples | Limitations | License Table of content Descriptio

Angel J. Ruiz Moreno 173 Dec 25, 2022
A collection of resources, problems, explanations and concepts that are/were important during my Data Science journey

Data Science Gurukul List of resources, interview questions, concepts I use for my Data Science work. Topics: Basics of Programming with Python + Unde

Smaranjit Ghose 10 Oct 25, 2022
Train SN-GAN with AdaBelief

SNGAN-AdaBelief Train a state-of-the-art spectral normalization GAN with AdaBelief https://github.com/juntang-zhuang/Adabelief-Optimizer Acknowledgeme

Juntang Zhuang 10 Jun 11, 2022
DVG-Face: Dual Variational Generation for Heterogeneous Face Recognition, TPAMI 2021

DVG-Face: Dual Variational Generation for HFR This repo is a PyTorch implementation of DVG-Face: Dual Variational Generation for Heterogeneous Face Re

52 Dec 30, 2022
The open source code of SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation.

SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation(ICPR 2020) Overview This code is for the paper: Spatial Attention U-Net for Retinal V

Changlu Guo 151 Dec 28, 2022
Trajectory Extraction of road users via Traffic Camera

Traffic Monitoring Citation The associated paper for this project will be published here as soon as possible. When using this software, please cite th

Julian Strosahl 14 Dec 17, 2022
Survival analysis in Python

What is survival analysis and why should I learn it? Survival analysis was originally developed and applied heavily by the actuarial and medical commu

Cameron Davidson-Pilon 2k Jan 08, 2023
Implementation of Sequence Generative Adversarial Nets with Policy Gradient

SeqGAN Requirements: Tensorflow r1.0.1 Python 2.7 CUDA 7.5+ (For GPU) Introduction Apply Generative Adversarial Nets to generating sequences of discre

Lantao Yu 2k Dec 29, 2022
MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification

MixText This repo contains codes for the following paper: Jiaao Chen, Zichao Yang, Diyi Yang: MixText: Linguistically-Informed Interpolation of Hidden

GT-SALT 309 Dec 12, 2022
Code for the paper titled "Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks" (NeurIPS 2021 Spotlight).

Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks This repository contains the code and pre-trained

Hassan Dbouk 7 Dec 05, 2022
PyTorch Implementation of Spatially Consistent Representation Learning(SCRL)

Spatially Consistent Representation Learning (CVPR'21) Official PyTorch implementation of Spatially Consistent Representation Learning (SCRL). This re

Kakao Brain 102 Nov 03, 2022
A NSFW content filter.

Project_Nfilter A NSFW content filter. With a motive of minimizing the spreads and leakage of NSFW contents on internet and access to others devices ,

1 Jan 20, 2022
Implementation of H-UCRL Algorithm

Implementation of H-UCRL Algorithm This repository is an implementation of the H-UCRL algorithm introduced in Curi, S., Berkenkamp, F., & Krause, A. (

Sebastian Curi 25 May 20, 2022
GANmouflage: 3D Object Nondetection with Texture Fields

GANmouflage: 3D Object Nondetection with Texture Fields Rui Guo1 Jasmine Collins

29 Aug 10, 2022
AdaDM: Enabling Normalization for Image Super-Resolution

AdaDM AdaDM: Enabling Normalization for Image Super-Resolution. You can apply BN, LN or GN in SR networks with our AdaDM. Pretrained models (EDSR*/RDN

58 Jan 08, 2023
Image processing in Python

scikit-image: Image processing in Python Website (including documentation): https://scikit-image.org/ Mailing list: https://mail.python.org/mailman3/l

Image Processing Toolbox for SciPy 5.2k Dec 31, 2022
Discovering Explanatory Sentences in Legal Case Decisions Using Pre-trained Language Models.

Statutory Interpretation Data Set This repository contains the data set created for the following research papers: Savelka, Jaromir, and Kevin D. Ashl

17 Dec 23, 2022
My implementation of Fully Convolutional Neural Networks in Keras

Keras-FCN This repository contains my implementation of Fully Convolutional Networks in Keras (Tensorflow backend). Currently, semantic segmentation c

The Duy Nguyen 15 Jan 13, 2020
Video Autoencoder: self-supervised disentanglement of 3D structure and motion

Video Autoencoder: self-supervised disentanglement of 3D structure and motion This repository contains the code (in PyTorch) for the model introduced

157 Dec 22, 2022
Implementation of "Fast and Flexible Temporal Point Processes with Triangular Maps" (Oral @ NeurIPS 2020)

Fast and Flexible Temporal Point Processes with Triangular Maps This repository includes a reference implementation of the algorithms described in "Fa

Oleksandr Shchur 20 Dec 02, 2022