Faculty · Seminars

ML@GT Seminar by Le Song (CSE) on “Embedding as a Tool for Algorithm Design” on April 5, 2017, 12:00n in EBB CHOA Room

Speaker: Le Song,  Computational Science and Engineering (CSE), GA Tech Location: Engineered Biosystems Building, CHOA Room (Map) Date/Time: April 5, 2017, 12:00n – 1:00pm (Lunch at 11:30am) Title: Embedding as a Tool for Algorithm Design Abstract: Many big data analytics problems are intrinsically complex and hard, making the design of effective and scalable algorithms very challenging.… Continue reading ML@GT Seminar by Le Song (CSE) on “Embedding as a Tool for Algorithm Design” on April 5, 2017, 12:00n in EBB CHOA Room

Faculty · Seminars

ML@GT Seminar by Manos Antonakakis (ECE) on “Using DNS & Machine Learning to Reason About Internet Abuse” at 12n on March 29, 2017, in Nano 1117/8

Speaker: Manos Antonakakis, School of Electrical and Computer Engineering (ECE), GA Tech Location: Marcus Nanotechnology 1117-1118 (Map) Date/Time: March 29, 2017, 12:00n – 1:00pm (Lunch at 11:30am)Title: Using DNS & Machine Learning to Reason About Internet Abuse Title: Using DNS & Machine Learning to Reason About Internet Abuse Abstract: The Domain Name System (DNS) is… Continue reading ML@GT Seminar by Manos Antonakakis (ECE) on “Using DNS & Machine Learning to Reason About Internet Abuse” at 12n on March 29, 2017, in Nano 1117/8

Seminars

ML@GT Seminar by Yao Xie on “Change-point detection meets machine learning”

ML@GT Seminar, part of the IRIM/ML@GT Seminar Series Speaker: Yao Xie (H. Milton Stewart School of Industrial and Systems Engineering, GA Tech) Location: Marcus Nanotechnology 1117-1118(Map) Date/Time: March 15, 2017, 12:00n – 1:00pm (Lunch at 11:30am) Title: Change-point detection meets machine learning Abstract: Change-point detection is a classic statistical framework for detecting a change in the… Continue reading ML@GT Seminar by Yao Xie on “Change-point detection meets machine learning”

Seminars

ECE Seminar: Greg Diamos (Baidu) on “Challenges and Opportunities in Deep Learning”

School of Electrical and Computer Engineering Seminar  Title. Challenges and Opportunities in Deep Learning Speaker: Greg Diamos (Baidu’s Silicon Valley AI Lab (SVAIL)) Date/Time/Location: Thursday, February 23rd from 11:00 – 12:00 PM in Van Leer C341 Abstract: Just this year, deep learning has fueled significant progress in computer vision, speech recognition, and natural language processing. We have… Continue reading ECE Seminar: Greg Diamos (Baidu) on “Challenges and Opportunities in Deep Learning”

Seminars

ML@GT Seminar by Justin Romberg on “Phase Retrieval meets Statistical Learning Theory”

Title: Phase Retrieval meets Statistical Learning Theory Speaker: Justin K. Romberg (School of Electrical and Computer Engineering, GA Tech) Date/Time: Wednesday 2/15/2017, 12n – 1pm, (Lunch at 11:30am) Location: TSRB Banquet Hall (85 5th Street NE, Atlanta, GA 30309) Abstract: We will present a new convex relaxation for the classic problem of phase retrieval, where we… Continue reading ML@GT Seminar by Justin Romberg on “Phase Retrieval meets Statistical Learning Theory”

Seminars

Robotics Seminar by Magnus Egerstedt on “Persistent Environmental Monitoring: Robots that Seemingly Do Nothing Most of the Time”

As part of the ML@GT/IRIM Seminar Series, here is the next very interesting talk by our very own, Magnus Egerstedt, Executive Director of IRIM. (Link) Date: Wednesday, February 8, 2016 Speaker: Magnus Egerstedt (Executive Director, IRIM) Title: “Persistent Environmental Monitoring: Robots that Seemingly Do Nothing Most of the Time” Location: Marcus Nanotechnology Building • Rooms… Continue reading Robotics Seminar by Magnus Egerstedt on “Persistent Environmental Monitoring: Robots that Seemingly Do Nothing Most of the Time”

Seminars

ML@GT Seminar by Jacob Eisentein (Georgia Tech) “Two new machine learning approaches for text classification”

Speaker: Jacob Eisenstein 12n-1pm, Wednesday, Jan 25, 2017 (Lunch will be served at 11:45am) Marcus Nano Rm 1117-1118 Title: Two new machine learning approaches for text classification Abstract: Text document classification is one of the most well-studied applications of machine learning. Yet this technology is still limited by practical difficulties and invalid underlying assumptions. First, many people who want… Continue reading ML@GT Seminar by Jacob Eisentein (Georgia Tech) “Two new machine learning approaches for text classification”