Safe corridor-based Trajectory Optimization (TO) presents an appealing
a...
Graph Neural Networks (GNNs) conduct message passing which aggregates lo...
Graph neural networks (GNNs) encounter significant computational challen...
Out-of-distribution (OOD) graph generalization are critical for many
rea...
A real-world text corpus sometimes comprises not only text documents but...
Recent advancements in Natural Language Processing (NLP) have led to the...
Maximal biclique enumeration is a fundamental problem in bipartite graph...
Heterogeneous graph neural networks (HGNNs) have exhibited exceptional
e...
Graph neural networks (GNNs) have demonstrated excellent performance in ...
Graph neural networks (GNNs) are a type of deep learning models that lea...
Diffusion models are a class of deep generative models that have shown
i...
Graph Neural Networks (GNNs) have achieved great success in various grap...
Graph Neural Networks (GNNs) have achieved great success in various task...
Selecting an appropriate response from many candidates given the utteran...
K-core decomposition is a commonly used metric to analyze graph structur...
The ensemble of deep neural networks has been shown, both theoretically ...
Message passing is the core of most graph models such as Graph Convoluti...
Graph neural networks (GNNs) have recently achieved state-of-the-art
per...
Graph Neural Networks (GNNs) have already been widely applied in various...
With the explosive growth of online information, recommender systems pla...