Semantic Learning

Semantic learning aims to equip machines with the ability to understand and utilize the meaning of data, going beyond simple pattern recognition. Current research focuses on improving semantic representation learning across diverse modalities (text, speech, images, 3D point clouds), often employing techniques like contrastive learning, multi-modal architectures, and hybrid models combining Euclidean and hyperbolic spaces to capture both semantic and hierarchical information. These advancements are driving progress in various applications, including zero-shot learning, improved knowledge graph reasoning, and enhanced performance in tasks like image segmentation, speech understanding, and clinical trial design.