Jeong YitaeNeural Scaling Laws on Graphs, do you believe is there strong related between model , data size…Correlation between model , data size and model performance at grpah data.Feb 25
InStanford CS224W: Machine Learning with GraphsbyRayan KanfarLearning Mesh-Based Flow Simulations on Graph NetworksTraditional deep learning methods are not able to model intricate mesh-based flow simulations accurately. In this post, we show a…Feb 9, 20222
Jeong YitaeGraph FDS | before we implement the model , we must know the trend of FDS paper — 04review _ Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud DetectionFeb 4Feb 4
Jan Jure StojanovičUsing Graph Neural Networks for pass prediction in the NFLPredicting outcomes on passing plays from the 2020/21 NFL season using the NFL Big Data Bowl dataset and Graph Neural Networks (GNNs).2d ago2d ago
Aizhan SmithTraditional ML methods with GraphsImagine you’re at a buzzing party. Every person (node) here has a story, and understanding them individually helps us grasp the party’s…Feb 1Feb 1
Jeong YitaeNeural Scaling Laws on Graphs, do you believe is there strong related between model , data size…Correlation between model , data size and model performance at grpah data.Feb 25
InStanford CS224W: Machine Learning with GraphsbyRayan KanfarLearning Mesh-Based Flow Simulations on Graph NetworksTraditional deep learning methods are not able to model intricate mesh-based flow simulations accurately. In this post, we show a…Feb 9, 20222
Jeong YitaeGraph FDS | before we implement the model , we must know the trend of FDS paper — 04review _ Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud DetectionFeb 4
Jan Jure StojanovičUsing Graph Neural Networks for pass prediction in the NFLPredicting outcomes on passing plays from the 2020/21 NFL season using the NFL Big Data Bowl dataset and Graph Neural Networks (GNNs).2d ago
Aizhan SmithTraditional ML methods with GraphsImagine you’re at a buzzing party. Every person (node) here has a story, and understanding them individually helps us grasp the party’s…Feb 1
InStanford CS224W: Machine Learning with GraphsbyAndre TuratiIncorporating Edge Features into Graph Neural Networks for Country GDP PredictionsBy Andre Turati, Peter Boennighausen, and Rahul Shiv as part of the Stanford CS224W course projectJan 18, 2022
Jeong YitaeGraph FDS | before we implement the model , we must know the trend of FDS paper — 03H2-FDetector: A GNN-based Fraud Detector with Homophilic and Heterophilic ConnectionsJan 29
InStanford CS224W: Machine Learning with GraphsbyAmelia WoodwardPredicting Los Angeles Traffic with Graph Neural NetworksBy Julie Wang, Amelia Woodward, Tracy Cai as part of the Stanford CS224W course project.Jan 16, 20223
