<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>neural networks | Georgios Exarchakis</title><link>https://exarchakis.net/tag/neural-networks/</link><atom:link href="https://exarchakis.net/tag/neural-networks/index.xml" rel="self" type="application/rss+xml"/><description>neural networks</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© 2026</copyright><lastBuildDate>Fri, 28 Dec 2018 00:00:00 +0000</lastBuildDate><image><url>https://exarchakis.net/media/icon_hua2ec155b4296a9c9791d015323e16eb5_11927_512x512_fill_lanczos_center_2.png</url><title>neural networks</title><link>https://exarchakis.net/tag/neural-networks/</link></image><item><title>Kymatio: Scattering Transforms in Python</title><link>https://exarchakis.net/publication/kymatio-scattering-transforms-in-python/</link><pubDate>Fri, 28 Dec 2018 00:00:00 +0000</pubDate><guid>https://exarchakis.net/publication/kymatio-scattering-transforms-in-python/</guid><description>&lt;!-- More detail can easily be written here using *Markdown* and $\rm \LaTeX$ math code. --></description></item><item><title>Probabilistic Models for Invariant Representations and Transformations</title><link>https://exarchakis.net/publication/probabilistic-models-for-invariant-representations-and-transformations/</link><pubDate>Thu, 01 Dec 2016 00:00:00 +0000</pubDate><guid>https://exarchakis.net/publication/probabilistic-models-for-invariant-representations-and-transformations/</guid><description>&lt;!-- More detail can easily be written here using *Markdown* and $\rm \LaTeX$ math code. --></description></item><item><title>Learning invariant features by harnessing the aperture problem</title><link>https://exarchakis.net/publication/learning-invariant-features-by-harnessing-the-aperture-problem/</link><pubDate>Wed, 05 Jun 2013 00:00:00 +0000</pubDate><guid>https://exarchakis.net/publication/learning-invariant-features-by-harnessing-the-aperture-problem/</guid><description>&lt;!-- More detail can easily be written here using *Markdown* and $\rm \LaTeX$ math code. --></description></item></channel></rss>