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 →

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 →

Seminar by Soumith Chintala on “An Overview of Deep Learning Frameworks and an Introduction to PyTorch” 9/6/17, 12:30, Nano 1118

ML@GT Seminar Series Title: An Overview of Deep Learning Frameworks and an Introduction to PyTorch Speaker: Soumith Chintala, Facebook Date/Time: Wednesday Sep 6 2017, 12:30 pm - 1:30 pm Location: Marcus Nanotechnology Building, Room 1118 Abstract: In this talk, you will get an exposure to the various types of deep learning frameworks – declarative and imperative frameworks such... Continue Reading →

ML@GT Distinguished Seminar by Pedro Domingo (UW) on “Sum-Product Networks: The Next Generation of Deep Models” 4/19/2017 @ 12n EBB

Title: "Sum-Product Networks: The Next Generation of Deep Models" Speaker: Pedro Domingo (University of Washington) Date/Time: April 19, 2017, @ 12n (lunch served at 11:30am) Location: Engineered Biosystems Building (EBB), CHOA Room Abstract: The two main types of deep learning are function approximation and probability estimation. Function approximators like convolutional neural networks are robust and allow for real-time inference,... Continue Reading →

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 →

Create a website or blog at

Up ↑

%d bloggers like this: