Lists (5)
Starred repositories
This software package implements the pytorch adaption of GATGNN-Voltage for the problem voltage prediction.
Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
Crystal graph convolutional neural networks for predicting material properties.
AbdurazaaqMohammed / AntiSplit-M
Forked from REAndroid/APKEditorApp to AntiSplit (merge) split APKs (APKS/XAPK/APKM) to regular .APK file on Android
Geometric GNN Dojo provides unified implementations and experiments to explore the design space of Geometric Graph Neural Networks (ICML 2023)
Implementation of E(n)-Equivariant Graph Neural Networks, in Pytorch
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Premium Spotify client for Windows 7-11. (ad-free; turn on/off auto-updates, podcasts and more..)
Graph Neural Network Library for PyTorch
SpotX patcher used for patching the desktop version of Spotify
Graph Neural Networks with Keras and Tensorflow 2.
Must-read papers on graph neural networks (GNN)
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ https://www.youtube.com/@dr_k_choudhary
A Google-Colab Notebook Collection for Materials Design: https://jarvis.nist.gov/
Jupyter notebooks demonstrating the utilization of open-source codes for the study of materials science.
Supplementary Materials for Tshitoyan et al. "Unsupervised word embeddings capture latent knowledge from materials science literature", Nature (2019).
A game theoretic approach to explain the output of any machine learning model.
This free RAM cleaner uses native Windows features to optimize memory areas. It's a compact, portable, and smart application.
ChenglongWang / OmniLearner
Forked from MannLabs/OmicLearnTransparent exploration of machine learning for structural data.
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.