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A sample model sheet from the DVD tutorial 'Chaos&Evolutions' In visual arts, a model sheet, also known as a character board, character sheet, character study or simply a study, is a document used to help standardize the appearance, poses, and gestures of a character in arts such as animation, comics, and video games.
[[Category:Fictional character infobox templates]] to the <includeonly> section at the bottom of that page. Otherwise, add <noinclude>[[Category:Fictional character infobox templates]]</noinclude> to the end of the template code, making sure it starts on the same line as the code's last character.
[[Category:Character templates]] to the <includeonly> section at the bottom of that page. Otherwise, add <noinclude>[[Category:Character templates]]</noinclude> to the end of the template code, making sure it starts on the same line as the code's last character.
Creates an infobox for a fictional character of any type. Template parameters [Edit template data] This template prefers block formatting of parameters. Parameter Description Type Status Name name Character name to display in top header (default = PAGENAME). String optional Series series Name of the series in which the character appears. String optional Franchise franchise Name of the ...
Template documentation For the maintenance tag, see Template:AI-generated . This template's initial visibility currently defaults to autocollapse , meaning that if there is another collapsible item on the page (a navbox, sidebar , or table with the collapsible attribute ), it is hidden apart from its title bar; if not, it is fully visible.
Character.ai was established in November 2021. [1] The company's co-founders, Noam Shazeer and Daniel de Freitas, were both engineers from Google. [7] While at Google, the co-founders both worked on AI-related projects: Shazeer was a lead author on a paper that Business Insider reported in April 2023 "has been widely cited as key to today's chatbots", [8] and Freitas was the lead designer of ...
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In theory, classic RNNs can keep track of arbitrary long-term dependencies in the input sequences. The problem with classic RNNs is computational (or practical) in nature: when training a classic RNN using back-propagation, the long-term gradients which are back-propagated can "vanish", meaning they can tend to zero due to very small numbers creeping into the computations, causing the model to ...