Building LEGO Using Deep Generative Models of Graphs

December 21, 2020 · Entered Twilight · 🏛 arXiv.org

"Last commit was 5.0 years ago (≥5 year threshold)"

Evidence collected by the PWNC Scanner

Repo contents: .gitignore, DGL_DGMG, DGL_GIN, DGMG_train.py, README.md, __init__.py, database, examples.ipynb, extract_dataset.py, helpers.py, permutation_script.py, pretrained_GIN.h5, pyFiles, requirements.txt, setup_train.py, valid_gen.py

Authors Rylee Thompson, Elahe Ghalebi, Terrance DeVries, Graham W. Taylor arXiv ID 2012.11543 Category cs.AI: Artificial Intelligence Cross-listed cs.LG Citations 23 Venue arXiv.org Repository https://github.com/uoguelph-mlrg/GenerativeLEGO ⭐ 56 Last Checked 1 month ago

Abstract

Generative models are now used to create a variety of high-quality digital artifacts. Yet their use in designing physical objects has received far less attention. In this paper, we advocate for the construction toy, LEGO, as a platform for developing generative models of sequential assembly. We develop a generative model based on graph-structured neural networks that can learn from human-built structures and produce visually compelling designs. Our code is released at: https://github.com/uoguelph-mlrg/GenerativeLEGO.