High-order Graph Neural Networks (HO-GNNs) have been developed to infer
...
In recent years, kernel methods are widespread in tasks of similarity
me...
In this work, we develop an Aligned Entropic Reproducing Kernel (AERK) f...
Quantum theory has shown its superiority in enhancing machine learning.
...
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...
Graph Neural Networks (GNNs) are recently proposed neural network struct...
Joint-event-extraction, which extracts structural information (i.e., ent...
In this paper, we develop a new graph kernel, namely the Hierarchical
Tr...
In this work, we develop a novel framework to measure the similarity bet...
We develop a novel method for measuring the similarity between complete
...
In this paper, we develop a novel Aligned-Spatial Graph Convolutional Ne...
Feature selection has been proven a powerful preprocessing step for
high...
In this paper, we develop a new aligned vertex convolutional network mod...
Feature selection can efficiently identify the most informative features...
In this paper, we develop a new Quantum Spatial Graph Convolutional Neur...
In this paper we present a differential approach to photo-polarimetric s...