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PRODID:-//Discrete Mathematics Group - ECPv6.15.20//NONSGML v1.0//EN
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METHOD:PUBLISH
X-ORIGINAL-URL:https://dimag.ibs.re.kr
X-WR-CALDESC:Events for Discrete Mathematics Group
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TZID:Asia/Seoul
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
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DTSTART:20180101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20190418T110000
DTEND;TZID=Asia/Seoul:20190418T120000
DTSTAMP:20260423T123748
CREATED:20190403T013055Z
LAST-MODIFIED:20240707T090539Z
UID:733-1555585200-1555588800@dimag.ibs.re.kr
SUMMARY:Jon-Lark Kim (김종락)\, Introduction to Boolean functions with Artificial Neural Network
DESCRIPTION:A Boolean function is a function from the set Q of binary vectors of length n (i.e.\, the binary n-dimensional hypercube) to $F_2=\{0\,1\}$. It has several applications to complexity theory\, digital circuits\, coding theory\, and cryptography.\nIn this talk we give a connection between Boolean functions and Artificial Neural Network. We describe how to represent Boolean functions by Artificial Neural Network including linear and polynomial threshold units and sigmoid units. For example\, even though a linear threshold function cannot realize XOR\, a polynomial threshold function can do it. We also give currently open problems related to the number of (Boolean) linear threshold functions and polynomial threshold functions.
URL:https://dimag.ibs.re.kr/event/2019-04-18/
LOCATION:Room B232\, IBS (기초과학연구원)
CATEGORIES:Discrete Math Seminar
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