This paper targets a novel trade-off problem in generalizable prompt lea...
Rare categories abound in a number of real-world networks and play a piv...
Long-tailed data distributions are prevalent in a variety of domains,
in...
While graph heterophily has been extensively studied in recent years, a
...
Long-tail data distributions are prevalent in many real-world networks,
...
Quantized Neural Networks (QNNs) receive increasing attention in
resourc...
The recent trend towards Personalized Federated Learning (PFL) has garne...
Transferring knowledge across graphs plays a pivotal role in many high-s...
Complementary recommendation gains increasing attention in e-commerce si...
Given a resource-rich source graph and a resource-scarce target graph, h...
Subgraph similarity search, one of the core problems in graph search,
co...
Adversarial training (AT) is proved to reliably improve network's robust...
With the development of online artificial intelligence systems, many dee...
Graph pre-training strategies have been attracting a surge of attention ...
Deep neural networks (DNNs) are found to be vulnerable to adversarial no...
Deep neural networks have been demonstrated to be vulnerable to adversar...
Deep neural networks (DNNs) are vulnerable to adversarial noise. A range...
Deep neural networks (DNNs) are vulnerable to adversarial noise. Their
a...
Deep neural networks (DNNs) are vulnerable to adversarial noise.
Preproc...