<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"
	xmlns:content="http://purl.org/rss/1.0/modules/content/"
	xmlns:wfw="http://wellformedweb.org/CommentAPI/"
	xmlns:dc="http://purl.org/dc/elements/1.1/"
	xmlns:atom="http://www.w3.org/2005/Atom"
	xmlns:sy="http://purl.org/rss/1.0/modules/syndication/"
	xmlns:slash="http://purl.org/rss/1.0/modules/slash/"
	>

<channel>
	<title>over-smoothing &#8211; BIOENGINEER.ORG</title>
	<atom:link href="https://bioengineer.org/tag/over-smoothing/feed/" rel="self" type="application/rss+xml" />
	<link>https://bioengineer.org</link>
	<description>Bioengineering</description>
	<lastBuildDate>Thu, 08 Oct 2026 01:03:12 +0000</lastBuildDate>
	<language>en-US</language>
	<sy:updatePeriod>
	hourly	</sy:updatePeriod>
	<sy:updateFrequency>
	1	</sy:updateFrequency>
	<generator>https://wordpress.org/?v=7.1.3</generator>

<image>
	<url>https://bioengineer.org/wp-content/uploads/2019/09/cropped-bioengineering-32x32.png</url>
	<title>over-smoothing &#8211; BIOENGINEER.ORG</title>
	<link>https://bioengineer.org</link>
	<width>32</width>
	<height>32</height>
</image> 
<site xmlns="com-wordpress:feed-additions:1">72741379</site>	<item>
		<title>New Graph AI Learns Without Gradient Descent, Cutting Training Time Dramatically</title>
		<link>https://bioengineer.org/new-graph-ai-learns-without-gradient-descent-cutting-training-time-dramatically/</link>
		
		<dc:creator><![CDATA[]]></dc:creator>
		<pubDate>Thu, 08 Oct 2026 01:03:12 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[Applied Intelligence]]></category>
		<category><![CDATA[autoencoder]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[Extreme Learning Machine]]></category>
		<category><![CDATA[gradient descent]]></category>
		<category><![CDATA[graph convolutional networks]]></category>
		<category><![CDATA[graph embedding]]></category>
		<category><![CDATA[link prediction]]></category>
		<category><![CDATA[node classification]]></category>
		<category><![CDATA[Open Graph Benchmark]]></category>
		<category><![CDATA[over-smoothing]]></category>
		<category><![CDATA[residual compensation]]></category>
		<guid isPermaLink="false">https://bioengineer.org/?p=406590</guid>

					<description><![CDATA[Researchers at Fuzhou University have developed RCGELM-AE, a graph embedding model that trains in closed form without gradient descent while outperforming conventional graph autoencoders on link prediction and node classification benchmarks.]]></description>
		
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">406590</post-id>	</item>
	</channel>
</rss>
