<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>rnn on Tom Roth</title><link>https://tomroth.dev/tags/rnn/</link><description>Recent content in rnn on Tom Roth</description><generator>Hugo -- gohugo.io</generator><language>en</language><lastBuildDate>Fri, 07 Oct 2022 00:00:00 +0000</lastBuildDate><atom:link href="https://tomroth.dev/tags/rnn/index.xml" rel="self" type="application/rss+xml"/><item><title>How does weight-tying work in a RNN?</title><link>https://tomroth.dev/weight_tying/</link><pubDate>Fri, 07 Oct 2022 00:00:00 +0000</pubDate><guid>https://tomroth.dev/weight_tying/</guid><description>Weight-tying is where you have a language model and use the same weight matrix for the input-to-embedding layer (the input embedding) and the hidden-to-softmax layer (the output embedding). The idea is that these two matrices contain essentially the same information, each having a row per word in the vocabulary. The concept seems to be first mentioned by Press and Wolf in 2016.
Below we illustrate this with a RNN. This post assumes that you are somewhat familiar already with how a RNN works.</description></item></channel></rss>