<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Matthieu Wyart | Stéphane d'Ascoli</title><link>https://stephane-dascoli.netlify.app/author/matthieu-wyart/</link><atom:link href="https://stephane-dascoli.netlify.app/author/matthieu-wyart/index.xml" rel="self" type="application/rss+xml"/><description>Matthieu Wyart</description><generator>Source Themes Academic (https://sourcethemes.com/academic/)</generator><language>en-us</language><lastBuildDate>Wed, 01 Jan 2020 00:00:00 +0000</lastBuildDate><image><url>https://stephane-dascoli.netlify.app/images/icon_huee8cdd0d6e28532bd05f8ab65b42238b_575095_512x512_fill_lanczos_center_2.png</url><title>Matthieu Wyart</title><link>https://stephane-dascoli.netlify.app/author/matthieu-wyart/</link></image><item><title>Scaling description of generalization with number of parameters in deep learning</title><link>https://stephane-dascoli.netlify.app/publication/scaling/</link><pubDate>Wed, 01 Jan 2020 00:00:00 +0000</pubDate><guid>https://stephane-dascoli.netlify.app/publication/scaling/</guid><description/></item><item><title>Jamming transition as a paradigm to understand the loss landscape of deep neural networks</title><link>https://stephane-dascoli.netlify.app/publication/jamming/</link><pubDate>Tue, 01 Jan 2019 00:00:00 +0000</pubDate><guid>https://stephane-dascoli.netlify.app/publication/jamming/</guid><description/></item></channel></rss>