Embodied Question Answering is a new AI task where an agent is spawned at a random location in a 3D environment and asked a question ("What color is the car?"). In order to answer, the agent must first intelligently navigate to explore the environment, gather information through first-person (egocentric) vision, and then answer the question ("orange").
This post summarizes the latest joint research between researchers at Georgia Tech and Facebook on using visualization to make sense of deep learning models, published at IEEE VIS’17, a top visualization conference. While powerful deep learning models have significantly improved prediction accuracy, understanding these models remains a big challenge. Deep learning models are more difficult… Continue reading Visualizing Deep Learning Models at Facebook
Please join us on Thursday 2/22/2018 for the ML@GT Center Spring Event featuring talks from internal faculty and invited speakers. Event runs from 10AM to 5PM in the Klaus Atrium. Invited Speakers Sanjeev Arora - Princeton University Sanjeev Arora is Charles C. Fitzmorris Professor of Computer Science at Princeton University. He is interested in… Continue reading ML@GT Spring Event – 2/22
Advances in deep learning of representation have resulted in powerful generative approaches on modeling continuous data like time series and images, but it is still challenging to correctly deal with discrete structured data, such as chemical molecules and computer programs. To tackle these challenges, there has been many improvements in formalization of structure generation that… Continue reading Syntax-Directed Variational Autoencoder for Structured Data
The list of accepted papers at ICLR 2018 was released last week and Machine Learning at Georgia Tech (ML@GT) had a strong presence. Out of 935 submissions, 23 oral and 314 conference papers were accepted (roughly 36%). We are pleased to announce that Georgia Tech had 10 conference papers this year, with 1 of them… Continue reading ICLR 2018 accepted papers and ML@GT
This past week, The Agency (the undergraduate ML club) was busy throwing a number of events to promote knowledge of Machine Learning and Deep Learning at Georgia Tech. On Thursday Nov 2, Raphael Gontijo Lopes, the president of the club, presented the first talk in a series entitled “A Deeper Dive into Deep Learning”. In… Continue reading Undergraduates Impact Campus with Deep Learning Talks and Competitions
We are pleased to have these new faculty join us starting this year. Siva Maguluri CoE / ISyE Devi Parikh CoC / IC Jacob Abernathy CoC / SCS Dhruv Batra CoC / IC Rachel Cummings CoE / ISyE Eva Dyer CoE / BME Wenjing Liao CoS / Math Thomas Ploetz CoC / IC Tuo Zhao… Continue reading Welcome to all the new Machine Learning Faculty to Georgia Tech
ML/Statistics Seminar Series Date/Time: Thursday Sep 28 2017, 11:00 am - 12:00 pmLocation: Advisory Boardroom, #402 Groseclose Speaker: Sham Kakade; Department of Statistics and Computer Science, University of Washington Title: Faster least squares and faster eigenvector computation: Improved algorithms for both optimization and statistics in the big data regime Abstract: Least squares and eigenvector computations… Continue reading ML/Statistics Seminar by Shama Kakade on “Faster least squares and faster eigenvector computation: Improved algorithms for both optimization and statistics in the big data regime”
ML@GT Seminar and Guest Speaker for CS 7643 Deep Learning Title: TF-Slim: A Lightweight Library for Defining, Training and Evaluating Complex Models in TensorFlow Speaker: Nathan Silberman Date/Time: Thursday Sep 7 2017, 4:30 pm - 5:45 pm Location: Clough 144 Abstract: TF-Slim is a TensorFlow-based library with various components. These include modules for easily defining neural network models… Continue reading Seminar by Nathan Silberman on “TF-Slim: A Lightweight Library for Defining, Training and Evaluating Complex Models in TensorFlow” Thursday Sep 7 2017, 4:30 pm – 5:45 pm in Clough 144
Machine Learning/Statistics Seminar Series Date/Time: Thursday Sep 7 2017, 11:00 pm - 12:00 pm Location: Advisory Boardroom, #402 Groseclose Speaker: Xiaodong Li Title: Convex Relaxation for Community Detection Abstract: Cluster structures are ubiquitous for large data, and community detection has recently attracted much research attention in applied physics, sociology, computer science and statistics due to… Continue reading ML/Statistics Seminar by Xiaodong Li on “Convex Relaxation for Community Detection”
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