1.0KMI - Nagoya Universityhttps://www.kmi.nagoya-u.ac.jp/engDeep learning and AdS/CFT - KMI - Nagoya Universityrich600338<blockquote class="wp-embedded-content" data-secret="LPj8NyMe6o"><a href="https://www.kmi.nagoya-u.ac.jp/eng/seminar/1072/">Deep learning and AdS/CFT</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://www.kmi.nagoya-u.ac.jp/eng/seminar/1072/embed/#?secret=LPj8NyMe6o" width="600" height="338" title="“Deep learning and AdS/CFT” — KMI - Nagoya University" data-secret="LPj8NyMe6o" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script type="text/javascript">
/* <![CDATA[ */
/*! This file is auto-generated */
!function(d,l){"use strict";l.querySelector&&d.addEventListener&&"undefined"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!/[^a-zA-Z0-9]/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret="'+t.secret+'"]'),o=l.querySelectorAll('blockquote[data-secret="'+t.secret+'"]'),c=new RegExp("^https?:$","i"),i=0;i<o.length;i++)o[i].style.display="none";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute("style"),"height"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):"link"===t.message&&(r=new URL(s.getAttribute("src")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener("message",d.wp.receiveEmbedMessage,!1),l.addEventListener("DOMContentLoaded",function(){for(var e,t,s=l.querySelectorAll("iframe.wp-embedded-content"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute("data-secret"))||(t=Math.random().toString(36).substring(2,12),e.src+="#?secret="+t,e.setAttribute("data-secret",t)),e.contentWindow.postMessage({message:"ready",secret:t},"*")},!1)))}(window,document);
/* ]]> */
</script>
Deep learning is an advanced technology for artificial intelligence, and the research on it is developing rapidly in these years. The concept is based on neural networks and learning algorithms, while the basis has a lot of similarity to physical concepts such as differential equations, discretization, statistical averaging and renormalization. Here we implement the deep neural network scheme into the AdS/CFT correspondence, a renowned quantum gravity formulation. The neural network is identified with the bulk gravity spacetime, and the input …