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vectorized representations

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"Vectorized representations" refers to numerical representations of information—such as text, tokens, token sequences, or other data structures—expressed in the form of vectors in one or more dimensions, typically for use in machine learning models or computational processing. Synonyms include embeddings, feature vectors, encoded vectors, numerical encodings, or the like.

Vectorized representations may be generated through processes such as word embedding, sentence embedding, feature extraction, or deep learning-based encoding and may reflect semantic, syntactic, structural, or contextual characteristics of the input data. Each vector may comprise a plurality of numerical values arranged in a fixed or variable length structure and may be processed using mathematical operations such as dot products, matrix multiplication, or distance functions. The vectorized representations may be input to one or more artificial intelligence systems, including neural networks, transformer models, encoder-decoder pipelines, attention mechanisms, or other computational models, to enable tasks such as classification, similarity analysis, prediction, generation, or the like. Vectorized representations may reside in high-dimensional latent spaces and may evolve during training or inference to capture nuanced relationships between pieces of information. (Defined in conjunction with ChatGPT 4o Version, July 15, 2025.)