<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Attention on</title><link>https://h30306.github.io/blog/zh-tw/tags/attention/</link><description>Recent content in Attention on</description><generator>Hugo -- gohugo.io</generator><language>zh-tw</language><managingEditor>hwork0511@gmail.com (Wei Yi Chung)</managingEditor><webMaster>hwork0511@gmail.com (Wei Yi Chung)</webMaster><copyright>© 2026 Wei Yi Chung</copyright><lastBuildDate>Fri, 01 Nov 2024 00:00:00 +0000</lastBuildDate><atom:link href="https://h30306.github.io/blog/zh-tw/tags/attention/index.xml" rel="self" type="application/rss+xml"/><item><title>Dynamic Traffic Network Representation Model for MRT Passenger Flow</title><link>https://h30306.github.io/blog/zh-tw/paper/2024-kbs-dynamic-traffic-network-representation/</link><pubDate>Fri, 01 Nov 2024 00:00:00 +0000</pubDate><author>hwork0511@gmail.com (Wei Yi Chung)</author><guid>https://h30306.github.io/blog/zh-tw/paper/2024-kbs-dynamic-traffic-network-representation/</guid><description>Knowledge-Based Systems 2024：BERT 動態站點表徵 + 階層式注意力模型，從時間、站點、路線三個面向預測捷運人流</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://h30306.github.io/blog/paper/2024-kbs-dynamic-traffic-network-representation/featured.png"/></item></channel></rss>