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  2. Word embedding - Wikipedia

    en.wikipedia.org/wiki/Word_embedding

    Outline of machine learning; In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.

  3. Knowledge graph embedding - Wikipedia

    en.wikipedia.org/wiki/Knowledge_graph_embedding

    The machine learning task for knowledge graph embedding that is more often used to evaluate the embedding accuracy of the models is the link prediction. [1] [3] [5] [6] [7] [18] Rossi et al. [5] produced an extensive benchmark of the models, but also other surveys produces similar results.

  4. Transformer (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Transformer_(deep_learning...

    seq2seq – Family of machine learning approaches; Perceiver – Variant of Transformer designed for multimodal data; Vision transformer – Machine learning model for vision processing; Large language model – Type of machine learning model; BERT (language model) – Series of language models developed by Google AI

  5. Machine learning: How embeddings make complex data simple - AOL

    www.aol.com/machine-learning-embeddings-complex...

    Working with non-numerical data can be tough, even for experienced data scientists. A typical machine learning model expects its features to be numbers, not words, emails, website pages, lists ...

  6. Spatial embedding - Wikipedia

    en.wikipedia.org/wiki/Spatial_embedding

    A single point of interest (POI) can be assigned multiple features that can be used in machine learning. These could be demographic, transportation, meteorological, or economic data, for example. When embedding single points, it is common to consider the entire set of available points as nodes in a graph. [10]

  7. Latent space - Wikipedia

    en.wikipedia.org/wiki/Latent_space

    These models learn the embeddings by leveraging statistical techniques and machine learning algorithms. Here are some commonly used embedding models: Word2Vec: [4] Word2Vec is a popular embedding model used in natural language processing (NLP). It learns word embeddings by training a neural network on a large corpus of text.

  8. t-distributed stochastic neighbor embedding - Wikipedia

    en.wikipedia.org/wiki/T-distributed_stochastic...

    t-distributed stochastic neighbor embedding (t-SNE) is a statistical method for visualizing high-dimensional data by giving each datapoint a location in a two or three-dimensional map. It is based on Stochastic Neighbor Embedding originally developed by Geoffrey Hinton and Sam Roweis, [ 1 ] where Laurens van der Maaten and Hinton proposed the t ...

  9. Sentence embedding - Wikipedia

    en.wikipedia.org/wiki/Sentence_embedding

    Outline of machine learning; ... a sentence embedding is a representation of a sentence as a vector of numbers which encodes meaningful semantic information. [1] [2 ...