<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Pantelis Elinas |</title><link>https://tiao.io/authors/pantelis-elinas/</link><atom:link href="https://tiao.io/authors/pantelis-elinas/index.xml" rel="self" type="application/rss+xml"/><description>Pantelis Elinas</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Mon, 01 Jun 2020 00:00:00 +0000</lastBuildDate><item><title>Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings</title><link>https://tiao.io/publications/vi-gcn-2/</link><pubDate>Mon, 01 Jun 2020 00:00:00 +0000</pubDate><guid>https://tiao.io/publications/vi-gcn-2/</guid><description>&lt;p&gt;This paper is a follow-up to our
, previously
presented at the NeurIPS2019 Graph Representation Learning Workshop, now with
significantly expanded experimental analyses.&lt;/p&gt;
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&lt;/script&gt;</description></item><item><title>Variational Graph Convolutional Networks</title><link>https://tiao.io/publications/vi-gcn-1/</link><pubDate>Sun, 01 Dec 2019 00:00:00 +0000</pubDate><guid>https://tiao.io/publications/vi-gcn-1/</guid><description/></item></channel></rss>