downeykking/graph-papers
Graph Neural Network, Self-Supervised Learning, Contrastive Learning, RecSys, Transformer Papers Reading Notes.
This project helps researchers and practitioners in machine learning stay updated on the latest advancements in Graph Neural Networks and self-supervised learning techniques. It compiles a curated list of academic papers, providing quick access to original research, associated code, and relevant discussions. The primary user would be a machine learning researcher, data scientist, or an AI engineer working on graph-structured data.
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Use this if you need a quick reference or a curated reading list for state-of-the-art research papers in graph neural networks, self-supervised learning, contrastive learning, or recommender systems.
Not ideal if you are looking for an executable code library or a tutorial to implement these techniques, as this is primarily a collection of research papers and their links.
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Oct 11, 2023
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