embedding
noun/ɪmˈbɛdɪŋ/ · /ɛmˈbɛdɪŋ/
Etymology From embed + -ing.
7 senses
- 1The act or process by which one thing is embedded in another.
- 2A continuous map which is also a homeomorphism between…
- 3An immersion which is also a topological embedding…
- 4A ring homomorphism between fields (the name deriving…
- 5A map between metric spaces which preserves distances up…
- 6An injective morphism in a concrete category which is…
- 7A representation of a unit of text (such as a word or…
The act or process by which one thing is embedded in another.
› A map which, in any of several technical…
Mathematics, Sciences, Topology A continuous map which is also a homeomorphism between its domain and its image (considered with the subspace topology induced by its codomain).
Mathematics, Sciences An immersion which is also a topological embedding; equivalently, a diffeomorphism whose image is a submanifold of its codomain.
Differential topology
Mathematics, Sciences A ring homomorphism between fields (the name deriving from the fact that all such maps are injective).
Field theory; Galois theory; field theory; Galois theory
Mathematical analysis, Mathematics, Sciences A map between metric spaces which preserves distances up to some scaling factor (called the distortion).
Category theory, Computing, Engineering, Mathematics, Natural sciences, Physical sciences, Sciences An injective morphism in a concrete category which is also initial.
A representation of a unit of text (such as a word or token) as a vector, which encodes the context in which it is used.
Machine learning; artificial intelligence; machine learning; artificial intelligence
- word embeddings
- “A major reason we chose to study word embeddings is that they have been spectacularly successful in the last few years in helping computers make sense of language,” said Arvind Narayanan, a computer scientist at Princeton University and the paper’s senior author.2017 April 13, Hannah Devlin, quoting Arvind Narayanan, “AI programs exhibit racial and gender biases, research reveals”, in The Guardian, →ISSN:
1 more example
- A few years ago, Dr. [Alexander] Huth noticed that particular pieces of these maps — so-called context embeddings, which capture the semantic features, or meanings, of phrases — could be used to predict how the brain lights up in response to language.2023 May 1, Oliver Whang, “A.I. Is Getting Better at Mind-Reading”, in The New York Times, New York, N.Y.: The New York Times Company, →ISSN, →OCLC, archived from the original on 17 May 2023: