<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Kaist on Jungbae Park</title><link>https://jungbaepark.github.io/blog/tags/kaist/</link><description>Recent content in Kaist on Jungbae Park</description><generator>Hugo -- 0.152.2</generator><language>en-us</language><lastBuildDate>Fri, 15 Feb 2019 00:00:00 +0000</lastBuildDate><atom:link href="https://jungbaepark.github.io/blog/tags/kaist/index.xml" rel="self" type="application/rss+xml"/><item><title>Attentional Control for Time-Series Data (Master's Thesis)</title><link>https://jungbaepark.github.io/blog/projects/attentional-control-timeseries/</link><pubDate>Fri, 15 Feb 2019 00:00:00 +0000</pubDate><guid>https://jungbaepark.github.io/blog/projects/attentional-control-timeseries/</guid><description>Master&amp;rsquo;s thesis at KAIST on attentional control for time-series classification and synthesis, solving the memory-based vs. memoryless trade-off for EEG signals. Oral presentation at IEEE SMC 2018.</description></item><item><title>Multi-Agent Cognitive Policy Learning through Competition</title><link>https://jungbaepark.github.io/blog/projects/multi-agent-reinforcement-learning/</link><pubDate>Tue, 15 Nov 2016 00:00:00 +0000</pubDate><guid>https://jungbaepark.github.io/blog/projects/multi-agent-reinforcement-learning/</guid><description>Undergraduate research at KAIST on multi-agent cognitive policy learning through competitive reinforcement learning, demonstrating emergence of complex behaviors. Won Best Paper Award at 2016 KIIS Conference.</description></item></channel></rss>