Vector Representation

Vector representation is the process of encoding data, such as words, images, or graphs, into numerical vectors that capture their essential features and relationships. Current research focuses on improving the efficiency and effectiveness of these representations, exploring various model architectures like graph neural networks, transformers, and autoencoders, and employing techniques such as contrastive learning and dimensionality reduction to optimize performance for specific tasks. This field is crucial for advancing numerous applications, including natural language processing, computer vision, drug discovery, and autonomous driving, by enabling efficient processing and analysis of complex data.