Statistics for topic graph-neural-networks
RepositoryStats tracks 595,858 Github repositories, of these 592 are tagged with the graph-neural-networks topic. The most common primary language for repositories using this topic is Python (433). Other languages include: Jupyter Notebook (74), C++ (11)
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Colab Notebooks covering deep learning tools for biomolecular structure prediction and design
Anomaly detection related books, papers, videos, and toolboxes
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Colab Notebooks covering deep learning tools for biomolecular structure prediction and design
TxGNN: Zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design
Code & data accompanying the NeurIPS 2020 paper "Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings".
Colab Notebooks covering deep learning tools for biomolecular structure prediction and design
SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
Colab Notebooks covering deep learning tools for biomolecular structure prediction and design
Mixed continous/categorical flow-matching model for de novo molecule generation.
The unravelsports package aims to aid researchers, analysts and enthusiasts by providing intermediary steps in the complex process of turning raw sports data into meaningful information and actionable...
[NeurIPS 2024] Official implementation for paper "Can Graph Learning Improve Planning in LLM-based Agents?"
Colab Notebooks covering deep learning tools for biomolecular structure prediction and design
A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks
Mixed continous/categorical flow-matching model for de novo molecule generation.
The official implementation of DiffAbXL benchmarked in the paper "Exploring Log-Likelihood Scores for Ranking Antibody Sequence Designs", formerly titled "Benchmarking Generative Models for Antibody D...
Official implementation for NeurIPS'24 paper "Generative Semi-supervised Graph Anomaly Detection"
[NeurIPS 2024] Official implementation for paper "Can Graph Learning Improve Planning in LLM-based Agents?"
[KDD'2024] "LLM4Graph: A Survey of Large Language Models for Graphs"
A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks
Colab Notebooks covering deep learning tools for biomolecular structure prediction and design
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
Anomaly detection related books, papers, videos, and toolboxes
[KDD'2024] "LLM4Graph: A Survey of Large Language Models for Graphs"
"GraphEdit: Large Language Models for Graph Structure Learning"
A Comprehensive Survey of Self-Supervised Learning for Recommendation
All graph/GNN papers accepted at the International Conference on Machine Learning (ICML) 2024.
Mixed continous/categorical flow-matching model for de novo molecule generation.