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Events for July 17, 2015
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Meet USC: Admission Presentation, Campus Tour, and Engineering TalkFri, Jul 17, 2015
Viterbi School of Engineering Undergraduate Admission
Receptions & Special Events
This half day program is designed for prospective freshmen and family members. Meet USC includes an information session on the University and the Admission process, a student led walking tour of campus, and a meeting with us in the Viterbi School. During the engineering session we will discuss the curriculum, research opportunities, hands-on projects, entrepreneurial support programs, and other aspects of the engineering school. Meet USC is designed to answer all of your questions about USC, the application process, and financial aid.
Reservations are required for Meet USC. This program occurs twice, once at 8:30 a.m. and again at 12:30 p.m. Please make sure to check availability and register online for the session you wish to attend. Also, remember to list an Engineering major as your "intended major" on the webform!Location: Ronald Tutor Campus Center (TCC) - USC Admission Office
Audiences: Prospective Undergrads and Families
Contact: Viterbi Admission
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AI SEMINAR
Fri, Jul 17, 2015 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Mesrob I. Ohannessian, Postdoctoral researcher at UC San Diego
Talk Title: Good-Turing rare probability estimation: When it does and doesn't work.
Series: AI Seminar
Abstract: The "missing mass" is the probability of all unseen symbols in i.i.d. samples from a discrete distribution. It captures a very fundamental notion of rare event. Being able to estimate this probability was critical in the wartime efforts of Alan Turing and his coworker Jack Good. Together, they proposed a very simple estimator that has been very influential to this day. In this talk, I will first overview the Good-Turing estimator and its favorable properties. I will then dismantle this impeccable image. In particular, I will show that Good-Turing can fail to learn the missing mass in relative error, for even the simplest light-tailed distributions. In fact, no other estimator can do this without further specifying the distribution class. I will then reconstruct a new reputation for this old estimator, as a highly effective specialized rare probability estimator for heavy-tailed distributions. This explains its success in areas where these distributions arise, such as in natural language modeling. This change in perspective opens the door to streamlined estimation techniques that are inspired by extreme value theory, and that extend far beyond missing mass estimation.
Biography: Mesrob I. Ohannessian is a postdoctoral researcher at UC San Diego. Previously, he spent two years in France, one at the Microsoft Research - Inria joint centre as a postdoc, and another at Université Paris-Sud as a Marie Curie Fellow under an ERCIM Alain Bensoussan Fellowship. He received his PhD in Electrical Engineering and Computer Science from MIT. His research interests are broadly in statistics, information theory, machine learning, and their applications, particularly to problems marked by data scarcity.
Host: Aram Galstyan
Webcast: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=55d2344730a54d739928f6a760f319511dLocation: Information Science Institute (ISI) - 1135 - 11th fl Large CR
WebCast Link: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=55d2344730a54d739928f6a760f319511d
Audiences: Everyone Is Invited
Contact: Alma Nava / Information Sciences Institute
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NL Seminar-Shift-Reduce CCG Parsing with a Dependency Model
Fri, Jul 17, 2015 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Wenduan Xu, University of Cambridge/ USC ISI Intern
Talk Title: Shift-Reduce CCG Parsing with a Dependency Model
Series: Natural Language Seminar
Abstract: CCG is able to derive typed dependency structures, providing a useful approximation to the underlying predicate-argument relations of -who did what to whom- and dependency structures form an integral part of CCG. In this talk, I will first cover some essential background on CCG, its dependency structures and CCG parsing; I will then discuss a recent dependency model we developed for shift-reduce CCG parsing. A challenge arises in this model from the fact that the oracle needs to keep track of exponentially many gold-standard derivations, which are all hidden. And we solve this by integrating a packed parse forest with the beam-search decoder and introduce a novel technique for querying an exponentially-sized oracle on-the-fly during beam-search decoding.
Biography: Wenduan Xu is a graduate student in Cambridge advised by Stephen Clark, working on CCG parsing.
Host: Nima Pourdamghani and Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Location: 6th Flr Conf Rm # 689 Marina Del Rey
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
