Visualizing Deep Learning Models at Facebook

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 →

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 →

Syntax-Directed Variational Autoencoder for Structured Data

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 →

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/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 →

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

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 →

ML/Statistics Seminar by Xiaodong Li on “Convex Relaxation for Community Detection”

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 →

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