Search Results for author:
Found 8 papers, 7 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.
CylinderTag: An Accurate and Flexible Marker for Cylinder-Shape Objects Pose Estimation Based on Projective Invariants
Experimental results demonstrate that the CylinderTag is a highly promising visual marker for use on cylindrical-like surfaces, thus offering important guidance for future research on high-precision visual localization of cylinder-shaped objects.
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.
Identification of Negative Transfers in Multitask Learning Using Surrogate Models
This problem is computationally challenging since the number of subsets grows exponentially with the number of source tasks; efficient heuristics for subset selection do not always capture the relationship between task subsets and multitask learning performances.
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).
Improved Regularization and Robustness for Fine-tuning in Neural Networks
We present a PAC-Bayes generalization bound that depends on the distance traveled in each layer during fine-tuning and the noise stability of the fine-tuned model.
