Translations:Word Embeddings/24/en: Difference between revisions

Latest revision as of 23:34, 27 April 2026

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* '''Negative sampling''' — instead of computing the full {{Term|softmax}}, the model contrasts the true context word against <math>k</math> randomly sampled "negative" words.
* '''Hierarchical {{Term|softmax}}''' — organises the vocabulary in a binary tree, reducing the {{Term|softmax}} cost from <math>O(V)</math> to <math>O(\log V)</math>.

Negative sampling — instead of computing the full softmax, the model contrasts the true context word against $$ k $$ randomly sampled "negative" words.
Hierarchical softmax — organises the vocabulary in a binary tree, reducing the softmax cost from $$ O(V) $$ to $O(\log V)$ .

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	* '''Negative sampling''' — instead of computing the full softmax, the model contrasts the true context word against <math>k</math> randomly sampled "negative" words.		* '''Negative sampling''' — instead of computing the full {{Term\|softmax}}, the model contrasts the true context word against <math>k</math> randomly sampled "negative" words.
	* '''Hierarchical softmax''' — organises the vocabulary in a binary tree, reducing the softmax cost from <math>O(V)</math> to <math>O(\log V)</math>.		* '''Hierarchical {{Term\|softmax}}''' — organises the vocabulary in a binary tree, reducing the {{Term\|softmax}} cost from <math>O(V)</math> to <math>O(\log V)</math>.