Reparameterization trick - technique used in stochastic gradient variational inference. Article “Reparameterization trick” in English Wikipedia has 33.2 points for quality (as of July 1, 2025). The article contains 8 references and 11 sections.
Since the creation of article “Reparameterization trick”, its content was written by 4 registered users of English Wikipedia and edited by 4 registered Wikipedia users in all languages.
The article is cited 8 times in English Wikipedia and cited 8 times in all languages.
The highest Authors Interest rank from 2001:
The highest popularity rank from 2008:
There is 1 language version for this article in the WikiRank database (of the considered 55 Wikipedia language editions).
The quality and popularity assessment was based on Wikipédia dumps from July 1, 2025 (including revision history and pageviews for previous years).
| Best Rank | English: #448016 03.2025 |
Global: #758507 03.2025 |
| Best Rank | English: #156930 09.2024 |
Global: #423993 09.2024 |
On 10 February 2026 in multilingual Wikipedia, Internet users most often read articles on the following topics: Jeffrey Epstein, Ghislaine Maxwell, Ilia Malinin, Bad Bunny, 2026 Winter Olympics, Epstein files, Jutta Leerdam, Jake Paul, Puerto Rico, Little Saint James.
In English Wikipedia the most popular articles on that day were: Bad Bunny, Jeffrey Epstein, Epstein files, Ilia Malinin, Savannah Guthrie, Ghislaine Maxwell, 2026 Winter Olympics, List of Super Bowl halftime shows, Eileen Gu, Jutta Leerdam.
The WikiRank project is intended for automatic relative evaluation of the articles in the various language versions of Wikipedia. At the moment the service allows to compare over 44 million Wikipedia articles in 55 languages. Quality scores of articles are based on Wikipedia dumps from July, 2025. When calculating current popularity and AI of articles data from June 2025 was taken into account. For historical values of popularity and AI WikiRank used data from 2001 to 2025... More information
$$Quality=1/c∑↙{i=1}↖c nm_i-RS=1/5(22.5+6.5+28.57+78.57+29.71)$$
where:
The results of the calculations may be slightly different because of rounded values of normalized metrics in formulas
