graph_nets
pytorch-GAT
| graph_nets | pytorch-GAT | |
|---|---|---|
| 2 | 14 | |
| 5,322 | 2,222 | |
| 0.0% | - | |
| 1.8 | 0.0 | |
| over 1 year ago | over 1 year ago | |
| Python | Jupyter Notebook | |
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
graph_nets
-
[D] Graph neural networks
You can also have a look at these later surveys that give an idea of the different types of GNNs. Also if you prefer Tensorflow you can use the Graph Nets library.
-
RL Agent Library to use graph in spaces
I don't know if any RL library includes an already implemented agent that can process graphs. However there are a number of deep learning frameworks that can help with the implementation of graph neural networks, especially Graph Nets (based on Tensorflow) and PyTorch Geometric. You might need to modify an existing RL agent to make use of one of these frameworks. If you are not familiar with GNNs you can look up these surveys. This article may also be of interest to you: it tackles graph-based environments, and the paper's code is available (it has a custom implementation of A2C and uses PyTorch Geometric -- btw it doesn't use Gym's space.graph since this feature is very recent in Gym).
pytorch-GAT
-
[D] Graph neural networks
This repo has a nice hands-on walkthrough: https://github.com/gordicaleksa/pytorch-GAT
-
[Discussion] Who are some good deep learning YouTubers?
Maybe: https://youtube.com/c/TheAIEpiphany
-
Any resources to learn MDPs and finally complex POMDPs?
I would suggest these three resources: 1: Richard Sutton’s book 2:Reinforcement learning lecture series by deep mind 3:Deep RL lectures by deep mind 4: spinning up by open Ai -> very good resource for key research papers. Try to read and then implement them 5: Practical RL channel by machine learning with Phil. -> good resource on getting to know how to implement deep RL algorithms 6: Research paper breakdown/ practical DL -> good channel to follow for how to understand latest research papers
- Open-source Graph Attention Network (GAT) project with transductive + inductive Jupiter notebooks !
- Obrazovanje
- Open-source Graph Attention Network (GAT) project with transductive + inductive Jupiter notebooks!
-
Graph Attention Network project walkthrough! (recently open-sourced <3 )
GitHub link: https://github.com/gordicaleksa/pytorch-GAT
- Show HN: I'm Open-Sourcing Graph Attention Network (GAT) PyTorch
What are some alternatives?
pytorch_geometric - Graph Neural Network Library for PyTorch
pytorch-seq2seq - Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.
sr-drl - Implementation of Symbolic Relational Deep Reinforcement Learning based on Graph Neural Networks
gan-vae-pretrained-pytorch - Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.
Keras - Deep Learning for humans
GAT - Graph Attention Networks (https://arxiv.org/abs/1710.10903)
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
CodeSearchNet - Datasets, tools, and benchmarks for representation learning of code.
TokenCut - (CVPR 2022) Pytorch implementation of "Self-supervised transformers for unsupervised object discovery using normalized cut"
pytorch-learn-reinforcement-learning - A collection of various RL algorithms like policy gradients, DQN and PPO. The goal of this repo will be to make it a go-to resource for learning about RL. How to visualize, debug and solve RL problems. I've additionally included playground.py for learning more about OpenAI gym, etc.
2D-Gaussian-Splatting - A 2D Gaussian Splatting paper for no obvious reasons. Enjoy!
ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.