Input Embeddings

Input embeddings are numerical representations of data points (e.g., words, images, numbers) used as input to machine learning models, aiming to capture essential features and relationships within the data. Current research focuses on improving embedding quality through techniques like frequency shifting for efficient representation learning, contrastive learning for refinement, and the use of mathematical priors for enhanced numerical representation. These advancements are crucial for improving model performance, efficiency, and robustness across various applications, including natural language processing, computer vision, and recommendation systems, particularly in large-scale models and resource-constrained environments.