https://marovi.ai/index.php?action=history&feed=atom&title=Translations%3ADropout_A_Simple_Way_to_Prevent_Overfitting%2F5%2FenTranslations:Dropout A Simple Way to Prevent Overfitting/5/en - Revision history2026-04-28T03:28:03ZRevision history for this page on the wikiMediaWiki 1.39.1https://marovi.ai/index.php?title=Translations:Dropout_A_Simple_Way_to_Prevent_Overfitting/5/en&diff=13286&oldid=prevFuzzyBot: Importing a new version from external source2026-04-27T21:37:43Z<p>Importing a new version from external source</p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 21:37, 27 April 2026</td>
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<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><del style="font-weight: bold; text-decoration: none;">Dropout </del>provides an efficient approximation to model combination. During each training step, each neuron (including input units) is retained with a probability <math>p</math> and dropped (set to zero) with probability <math>1 - p</math>. This means that on each training case, a different "thinned" sub-network is sampled. At test time, all neurons are used but their outputs are scaled by <math>p</math> to approximate the expected output of the ensemble.</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><ins style="font-weight: bold; text-decoration: none;">{{Term|dropout}} </ins>provides an efficient approximation to model combination. During each training step, each neuron (including input units) is retained with a probability <math>p</math> and dropped (set to zero) with probability <math>1 - p</math>. This means that on each training case, a different "thinned" sub-network is sampled. At test time, all neurons are used but their outputs are scaled by <math>p</math> to approximate the expected output of the ensemble.</div></td></tr>
</table>FuzzyBothttps://marovi.ai/index.php?title=Translations:Dropout_A_Simple_Way_to_Prevent_Overfitting/5/en&diff=3140&oldid=prevFuzzyBot: Importing a new version from external source2026-04-27T00:32:07Z<p>Importing a new version from external source</p>
<p><b>New page</b></p><div>Dropout provides an efficient approximation to model combination. During each training step, each neuron (including input units) is retained with a probability <math>p</math> and dropped (set to zero) with probability <math>1 - p</math>. This means that on each training case, a different "thinned" sub-network is sampled. At test time, all neurons are used but their outputs are scaled by <math>p</math> to approximate the expected output of the ensemble.</div>FuzzyBot