<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Bayesian Deep Learning |</title><link>https://tiao.io/tags/bayesian-deep-learning/</link><atom:link href="https://tiao.io/tags/bayesian-deep-learning/index.xml" rel="self" type="application/rss+xml"/><description>Bayesian Deep Learning</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Thu, 01 Jun 2017 00:00:00 +0000</lastBuildDate><image><url>https://tiao.io/media/icon_hu_9c2a75fde2335590.png</url><title>Bayesian Deep Learning</title><link>https://tiao.io/tags/bayesian-deep-learning/</link></image><item><title>Aboleth</title><link>https://tiao.io/projects/aboleth/</link><pubDate>Thu, 01 Jun 2017 00:00:00 +0000</pubDate><guid>https://tiao.io/projects/aboleth/</guid><description>&lt;p&gt;
is a minimalistic
TensorFlow framework for scalable
and
approximation, focused on
techniques. Built at CSIRO Data61
with Daniel Steinberg and Lachlan McCalman.&lt;/p&gt;</description></item></channel></rss>