Hyperbolic geometry have shown significant potential in modeling complex structured data,
particularly those with underlying tree-like and hierarchical structures. Despite the …

Many significant problems involving crystal property prediction from 3D structures have
limited labeled data due to expensive and time-consuming physical simulations or lab …

Discrimination against minority groups within the banking sector has long resulted in
unequal treatment in financial services. Recent works in the general machine learning …

The prevalence of tree-like structures, encompassing hierarchical structures and power law
distributions, exists extensively in real-world applications, including recommendation …

Abstract Knowledge graph completion is an advanced artificial intelligence (AI) methodology
that enables the systematic organization and structuring of data. It can significantly enhance …

Existing unsupervised deep product quantization methods primarily aim for the increased
similarity between different views of the identical image, whereas the delicate multi-level …

Graph Active Learning (GAL), which aims to find the most informative nodes in graphs for
annotation to maximize the Graph Neural Networks (GNNs) performance, has attracted …

Abstract Knowledge graph completion plays a pivotal role in the era of artificial intelligence.
To harness the interpretability benefits of logical rules, we propose a cross-level position …

Personalized Federated Learning (PFL) has gained attention for privacy-preserving training
on heterogeneous data. However, existing methods fail to capture the unique inherent …

Representing knowledge in a diagrammatic form has been a long-standing goal of
humanity. Early efforts in the field of knowledge representation, such as symbolic logic and …