ml-research/cna_modules
Cluster-Normalize-Activate Modules
This project helps machine learning researchers improve the performance and robustness of Graph Neural Networks (GNNs) for tasks like node classification and property prediction on various graph datasets. It takes as input graph data (e.g., citation networks, social networks) and outputs optimized GNN models with enhanced learning capabilities. Researchers and practitioners working with GNNs for complex graph analysis will find this useful.
No commits in the last 6 months.
Use this if you are a researcher or advanced practitioner experimenting with Graph Neural Networks and want to apply state-of-the-art modules to enhance their performance on tasks like classifying nodes or predicting node properties within a graph.
Not ideal if you are looking for a plug-and-play solution for general data analysis or if you are not familiar with machine learning experimentation and Python development.
Stars
13
Forks
4
Language
Python
License
MIT
Category
Last pushed
Jan 13, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/ml-research/cna_modules"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
fangwei123456/spikingjelly
SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch.
neuromorphs/NIR
Neuromorphic Intermediate Representation reference implementation
BindsNET/bindsnet
Simulation of spiking neural networks (SNNs) using PyTorch.
norse/norse
Deep learning with spiking neural networks (SNNs) in PyTorch.
jeshraghian/snntorch
Deep and online learning with spiking neural networks in Python