Graph neural networks for road safety modeling: datasets and evaluations for accident analysis
We consider the problem of traffic accident analysis on a road network based on road
network connections and traffic volume. Previous works have designed various deep …
network connections and traffic volume. Previous works have designed various deep …
[HTML][HTML] Real-time forest fire detection with lightweight CNN using hierarchical multi-task knowledge distillation
Forest fires pose a significant threat to ecosystems, property, and human life, making their
early and accurate detection crucial for effective intervention. This study presents a novel …
early and accurate detection crucial for effective intervention. This study presents a novel …
Scalable Multitask Learning Using Gradient-based Estimation of Task Affinity
Multitask learning is a widely used paradigm for training models on diverse tasks, with
applications ranging from graph neural networks to language model fine-tuning. Since tasks …
applications ranging from graph neural networks to language model fine-tuning. Since tasks …
Boosting multitask learning on graphs through higher-order task affinities
Predicting node labels on a given graph is a widely studied problem with many applications,
including community detection and molecular graph prediction. This paper considers …
including community detection and molecular graph prediction. This paper considers …