Integrating complementary strengths of raw data and logical rules to improve the learning
generalization has been recently shown promising and effective, eg, abductive learning is …

Neuro-symbolic hybrid systems are promising for integrating machine learning and symbolic
reasoning, where perception models are facilitated with information inferred from a symbolic …

Abstract Optical Character Recognition (OCR) of historical document images remains a
challenging task because of the distorted input images, extensive number of uncommon …

Abstract Machine learning is widely used in dentistry nowadays, offering efficient solutions
for diagnosing dental diseases, such as periodontitis and gingivitis. Most existing methods …

Uniform Interpolation (UI) is an advanced non-standard reasoning service that seeks to
refine ontologies by creating rewritten modules. These modules, known as uniform …

Recent learning-to-imitation methods have shown promising results in planning via imitating
within the observation-action space. However, their ability in open environments remains …

Neuro-symbolic approaches have garnered much interest recently as a path toward
endowing neural systems with robust reasoning capabilities. Most proposed end-to-end …

Despite their widespread use, machine learning (ML) methods often exhibit sub-optimal
performance. The accuracy of these models is primarily hindered by insufficient training data …