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Embedding

Appears in 3 papers

A dense, low-dimensional vector representation of something — a word,

As used in Paper 05 — Efficient Estimation of Word Representations in Vector Space (Word2Vec) →

A dense, low-dimensional vector representation of something — a word,

As used in Paper 06 — Sequence to Sequence Learning with Neural Networks →

A dense, low-dimensional vector representation of a word. Introduced

As used in Paper 20 — Gemini: A Family of Highly Capable Multimodal Models →

A learned vector representation of a token (text word, image patch, audio feature, etc.). For Gemini, all embeddings are d_model = 2048 dimensions, ensuring uniform treatment.

Appears in papers

Paper 05 — Efficient Estimation of Word Representations in Vector Space (Word2Vec) → Paper 06 — Sequence to Sequence Learning with Neural Networks → Paper 20 — Gemini: A Family of Highly Capable Multimodal Models →