The phrase "embedding vector" is correct and can be used in written English.
It is commonly used in fields such as computer science, linguistics, and machine learning. Example: "The algorithm generated an embedding vector for each word in the dataset, allowing for more accurate natural language processing tasks."
Values transferred in the function call are the embedding vectors v (i), dimension m, time delay t, size of neighbourhood ε and norm to be used (Euclidean).
Thus, the distance data are recycled to allow creating a number of embedding vectors equal to the number of contour lines points.
Note that ψ l (x i ) for 1≤l≤m(t) is the ith coordinate of ψ l, the lth eigenvector of P. The Nyström extension [51, 54] is a numerically efficient approach to extend the embedding vectors {ψ l } defined on a set (Omega ={mathbf {x}_{i}}_{i=1}^{L}) to a sample point (tilde {mathbf {x}}notin Omega ).
This reduces the redundancy in the embedded vector.
However, the mir30 embedded vector was significantly less toxic to both PC12 and SH-SY5Y cells.
Topic models can be classified into statistical semantic models [3 10] and embedded vector models [11 13].
In this paper, we use a four-latent-layer recurrent neural network to compute the embedded vector for each document, and the structure of the proposed neural network is in Fig. 1.
In recent years, embedded vector models, such as Word2vec [11], GloVe [12], and DSSM [13], are more and more popular, and deep learning for NLP has attracted more and more researchers.
While capturing the semantics of documents, statistical semantic model computes the similarities between documents with co-occurrence matrix of terms, and embedded vector model uses neighbor(s) to represent the meaning of a target term; however, both of them cannot describe the term orders in a document.
Having acoustic and visual streams of feature frames and extracted from pairs of corresponding AV signals s and V (see Section 4.1), a context-dependent AV associator can be trained using a suitable non-linear function approximator: V ̂ ( k ) = h ( S e ( k ) ), where Se ( k ) = E ( S ( k − K / 2 − 1 : k + K / 2 ) ) is an embedded vector obtained from a context of K audio frames around frame k.
