<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Algorithm on SciDraft | A Hugo Theme</title><link>https://www.zhumengyao.com/scidraft/tags/algorithm/</link><description>Recent content in Algorithm on SciDraft | A Hugo Theme</description><generator>Hugo</generator><language>en</language><lastBuildDate>Mon, 21 Sep 2026 14:52:00 +0900</lastBuildDate><atom:link href="https://www.zhumengyao.com/scidraft/tags/algorithm/index.xml" rel="self" type="application/rss+xml"/><item><title>Masked Softmax Attention (algorithm)</title><link>https://www.zhumengyao.com/scidraft/notes/demo-note-algorithm-masked-attention/</link><pubDate>Mon, 21 Sep 2026 14:52:00 +0900</pubDate><guid>https://www.zhumengyao.com/scidraft/notes/demo-note-algorithm-masked-attention/</guid><description>&lt;p&gt;&lt;strong&gt;Demo note.&lt;/strong&gt; This note demonstrates what the &lt;strong&gt;SciDraft&lt;/strong&gt; Hugo theme can do
for academic and scientific publishing — for research institutions, researchers
and students. Its content is demonstration material only: readers should ignore
whether the demo content is correct.&lt;/p&gt;
&lt;p&gt;An algorithm is a procedure with a specification, and this one says how the attention
definition is executed on a machine: scale the scores, remove the positions that are not
allowed, normalise and mix.&lt;/p&gt;</description></item><item><title>Gradient Descent with Momentum (algorithm)</title><link>https://www.zhumengyao.com/scidraft/notes/demo-note-algorithm-momentum/</link><pubDate>Mon, 21 Sep 2026 10:32:00 +0900</pubDate><guid>https://www.zhumengyao.com/scidraft/notes/demo-note-algorithm-momentum/</guid><description>&lt;p&gt;&lt;strong&gt;Demo note.&lt;/strong&gt; This note demonstrates what the &lt;strong&gt;SciDraft&lt;/strong&gt; Hugo theme can do
for academic and scientific publishing — for research institutions, researchers
and students. Its content is demonstration material only: readers should ignore
whether the demo content is correct.&lt;/p&gt;
&lt;p&gt;The simplest optimiser with a memory is gradient descent with momentum: keep a running
velocity, add the gradient to it, and step in the direction of the velocity rather than the
gradient. The algorithm is short, and the behaviour it introduces is worth the two extra
lines.&lt;/p&gt;</description></item></channel></rss>