Linear Transformation

Linear transformations, fundamental mathematical operations that map vectors from one space to another, are central to numerous fields, with current research focusing on their applications in diverse areas such as signal processing, machine learning, and optical computing. Active research explores the use of linear transformations within neural networks, including their role in improving training efficiency, approximating complex models like transformers, and enabling cross-lingual analysis. These advancements have significant implications for enhancing model performance, developing more efficient algorithms, and creating novel applications in areas like image encryption and knowledge graph embedding.