gnn-jet-autoencoder

Graph neural network autoencoders for jets in HEP

https://github.com/zichunhao/gnn-jet-autoencoder

Science Score: 54.0%

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    Low similarity (7.2%) to scientific vocabulary

Keywords

anomaly-detection autoencoder compression deep-learning graph-neural-network graph-neural-networks high-energy-physics machine-learning message-passing-neural-network particle-physics permutation-equivariant permutation-invariance pytorch
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Repository

Graph neural network autoencoders for jets in HEP

Basic Info
  • Host: GitHub
  • Owner: zichunhao
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 635 KB
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 2
Topics
anomaly-detection autoencoder compression deep-learning graph-neural-network graph-neural-networks high-energy-physics machine-learning message-passing-neural-network particle-physics permutation-equivariant permutation-invariance pytorch
Created over 3 years ago · Last pushed almost 3 years ago
Metadata Files
Readme License Citation

README.md

Graph Neural Network Autoencoders Autoencoder for Jets

DOI

Overview

A graph autoencoder (GNNAE) for jets in particle physics implemented in PyTorch, mainly used as a baseline for LGAE

Data

To download data: 1. Install JetNet: pip3 install jetnet; 2. Run preprocess.py python utils/data/preprocess.py \ --jet-types g q t w z \ --save-dir "./data"

Training

To train the model, run train.py. An example is provided in examples/train.sh.

Architecture

Both the encoder and decoder are built upon the GraphNet architecture implemented in models/graphnet.py, which is a fully connected massage passing neural network. The message passing step of GraphNet is shown in the diagram below. Here, $d$ is any distance function, and EdgeNet and NodeNet are edge and node functions at the $t$-th message passing step, respectively, both of which are MLPs with LeakyReLU activation.

Owner

  • Name: Zichun Hao
  • Login: zichunhao
  • Kind: user
  • Company: University of California, San Diego

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: Hao
    given-names: Zichun
    orcid: https://orcid.org/0000-0002-5624-4907
  - family-names: Kansal
    given-names: Raghav
    orcid: https://orcid.org/0000-0003-2445-1060
  - family-names: Duarte
    given-names: Javier
    orcid: https://orcid.org/0000-0002-5076-7096
  - family-names: Chernyavskaya
    given-names: Nadezda
    orcid: https://orcid.org/0000-0002-2264-2229
title: "Graph Neural Network Autoencoders for Jets"
version: 1.0.0
doi: 10.5281/zenodo.7453764
date-released: 2022-12-17

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