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The running time needed to obtain the representations is also a factor to be considered.
The only desideratum in the representations is that similar words have similar vectors, according to euclidian distance or cosine similarity.
In Table 7, each of the representations is ranked with respect to (a) the original images and (b) the combined illumination errors and noise types, while Table 8 combines the aforementioned results into a single ranking.
Regarding computation time and resources, we argue that the main difference between the approaches lie in the item similarity calculation, since the size of the representations is significantly different.
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Guardian Australia understands the representations were made by the NSW police and government.
The representations were of a lone, uniformed nurse, smiling benignly at her patient – a scribbly, but unmistakably beatific presence.
The representations are "all originally completely uninhibited" (SW V: 338).
Both the representations are obtained using deep learning methods.
The validity and reliability of the representations are discussed.
The representations are not unitarizable (i.e., are not equivalent to Hermitian representations in Hilbert space).
For most psychologists, the controversy is not over whether there exist such representations, but over what the representations are like.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com