In this work, we develop an Aligned Entropic Reproducing Kernel (AERK) f...
In this paper, we propose a novel graph kernel, namely the Quantum-based...
In this work, we propose a family of novel quantum kernels, namely the
H...
To alleviate the challenges of building Knowledge Graphs (KG) from scrat...
Deep learning is attracting interest across a variety of domains, includ...
Event extraction, the technology that aims to automatically get the
stru...
Graph attention networks (GATs) have been recognized as powerful tools f...
Joint-event-extraction, which extracts structural information (i.e., ent...
The standardization process of the fifth generation (5G) wireless
commun...
In this paper, we develop a new graph kernel, namely the Hierarchical
Tr...
We formalize networks with evolving structures as temporal networks and
...
Multi-label learning studies the problem where an instance is associated...
In this work, we develop a novel framework to measure the similarity bet...
Counterfactual thinking describes a psychological phenomenon that people...
We develop a novel method for measuring the similarity between complete
...
Feature selection has been proven a powerful preprocessing step for
high...
In this paper, we develop a new aligned vertex convolutional network mod...
Most recent works model the market structure of the stock market as a
co...
Feature selection can efficiently identify the most informative features...
In this paper, we develop a new Quantum Spatial Graph Convolutional Neur...