How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
This project is a collection of deep learning research papers translated into annotated code. It serves as a resource for reproducing academic research, providing implementations of transformers, diffusion models, and reinforcement learning architectures. The library distinguishes itself by using a side-by-side annotation format that combines executable Python code with descriptive markdown notes. This approach provides a structured way to explain the logic of neural network papers alongside their PyTorch-based implementations. The codebase covers several major capability areas, including ge
This is the Pytorch implementation of NeurIPS-23 work: "Structure-free Graph Condensation (SFGC): From Large-scale Graphs to Condensed Graph-free Data".
KDD 2022 The implementation for "Condensing Graphs via One-Step Gradient Matching" on graph classification is shown below. For node classification, please refer to link.
One can use the Colab to evaluate our latest models.
GraphSlim, a PyTorch Library for Graph Reduction.
The main features of emory-melody/graphslim are: Graph Neural Networks.
Projects with overlapping indexed features include: labmlai/annotated_deep_learning_paper_implementations — This project is a collection of deep learning research papers translated into annotated code. It serves as a resource… amanda-zheng/sfgc — This is the Pytorch implementation of NeurIPS-23 work: "Structure-free Graph Condensation (SFGC): From Large-scale… amazon-research/doscond — [KDD 2022] The implementation for "Condensing Graphs via One-Step Gradient Matching" on graph classification is shown… benedekrozemberczki/appnp — A PyTorch implementation of "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019). benedekrozemberczki/attentionwalk — A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018). a4bio/proteininvbench — One can use the Colab to evaluate our latest models.