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Found 5 papers, 4 papers with code
Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysis
This paper constructs a large-scale, unified dataset of traffic accident records from official reports of various states in the US, totaling 9 million records, accompanied by road networks and traffic volume reports.
Boosting Multitask Learning on Graphs through Higher-Order Task Affinities
Lastly, we provide a theoretical analysis to show that under a planted block model of tasks on graphs, our affinity scores can provably separate tasks into groups.
Noise Stability Optimization for Flat Minima with Tight Rates
It leads to a 17. 7% (and 12. 8%) reduction in the trace (and largest eigenvalue) of the Hessian matrix of the loss surface.
Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion
Graph neural networks are widely used tools for graph prediction tasks.
Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees
We study the generalization properties of fine-tuning to understand the problem of overfitting, which has often been observed (e. g., when the target dataset is small or when the training labels are noisy).
