https://marovi.ai/index.php?action=history&feed=atom&title=Translations%3AAdam_A_Method_for_Stochastic_Optimization%2F19%2Fen 2026-04-28T01:54:24Z Revision history for this page on the wiki MediaWiki 1.39.1 https://marovi.ai/index.php?title=Translations:Adam_A_Method_for_Stochastic_Optimization/19/en&diff=13254&oldid=prev 2026-04-27T21:37:18Z

<p>Importing a new version from external source</p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="en"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 21:37, 27 April 2026</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1">Line 1:</td> <td colspan="2" class="diff-lineno">Line 1:</td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>The first moment estimate provides momentum-like behavior, accelerating convergence along consistent gradient directions. The second moment estimate scales the learning rate inversely with the root-mean-square of recent gradients, giving each parameter its own effective learning rate. The combination means parameters with consistently large gradients receive smaller updates, while parameters with small or noisy gradients receive relatively larger updates.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>The first moment estimate provides <ins style="font-weight: bold; text-decoration: none;">{{Term|</ins>momentum<ins style="font-weight: bold; text-decoration: none;">}}</ins>-like behavior, accelerating <ins style="font-weight: bold; text-decoration: none;">{{Term|</ins>convergence<ins style="font-weight: bold; text-decoration: none;">}} </ins>along consistent gradient directions. The second moment estimate scales the <ins style="font-weight: bold; text-decoration: none;">{{Term|</ins>learning rate<ins style="font-weight: bold; text-decoration: none;">}} </ins>inversely with the root-mean-square of recent gradients, giving each parameter its own effective <ins style="font-weight: bold; text-decoration: none;">{{Term|</ins>learning rate<ins style="font-weight: bold; text-decoration: none;">}}</ins>. The combination means parameters with consistently large gradients receive smaller updates, while parameters with small or noisy gradients receive relatively larger updates.</div></td></tr> <!-- diff cache key mediawiki:diff::1.12:old-2977:rev-13254 --> </table>

FuzzyBot https://marovi.ai/index.php?title=Translations:Adam_A_Method_for_Stochastic_Optimization/19/en&diff=2977&oldid=prev 2026-04-27T00:31:19Z

<p>Importing a new version from external source</p> <p><b>New page</b></p><div>The first moment estimate provides momentum-like behavior, accelerating convergence along consistent gradient directions. The second moment estimate scales the learning rate inversely with the root-mean-square of recent gradients, giving each parameter its own effective learning rate. The combination means parameters with consistently large gradients receive smaller updates, while parameters with small or noisy gradients receive relatively larger updates.</div>

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