<?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>Definition on SciDraft | A Hugo Theme</title><link>https://www.zhumengyao.com/scidraft/tags/definition/</link><description>Recent content in Definition on SciDraft | A Hugo Theme</description><generator>Hugo</generator><language>en</language><lastBuildDate>Mon, 21 Sep 2026 09:12:00 +0900</lastBuildDate><atom:link href="https://www.zhumengyao.com/scidraft/tags/definition/index.xml" rel="self" type="application/rss+xml"/><item><title>Scaled Dot-Product Attention (definition)</title><link>https://www.zhumengyao.com/scidraft/notes/demo-note-definition-attention/</link><pubDate>Mon, 21 Sep 2026 09:12:00 +0900</pubDate><guid>https://www.zhumengyao.com/scidraft/notes/demo-note-definition-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;Definitions in this subject fix notation rather than prove anything, and attention is
the clearest case: three projections, one score matrix, one weighted average. Every
implementation, diagram and proof in the rest of the literature is written against this
one paragraph of notation.&lt;/p&gt;</description></item><item><title>Cross-Entropy (definition)</title><link>https://www.zhumengyao.com/scidraft/notes/demo-note-definition-cross-entropy/</link><pubDate>Mon, 21 Sep 2026 09:02:00 +0900</pubDate><guid>https://www.zhumengyao.com/scidraft/notes/demo-note-definition-cross-entropy/</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 training objective of a language model has a name and a formula, and both matter
when the loss curve is read: cross-entropy is the expected negative log-probability of
what actually happened, measured in nats.&lt;/p&gt;</description></item></channel></rss>