Exploring Hypercomplex Spaces in Deep Learning for Enhanced Semantic Embedding

This research delves into a novel semantic embedding framework utilizing hypercomplex spaces. It encompasses theoretical analysis, algorithm design, experimental validation, and practical applications, aiming to optimize neural networks for improved semantic similarity computation and classification tasks across various datasets.