The Minds of the New Machines | Research Horizons | Georgia Tech’s Research News

Georgia Tech's Research Horizons Magazine has done a very nice write-up of the ML@GT center, featuring many of our research projects. Machine learning has been around for decades, but the advent of big data and more powerful computers has increased its impact significantly — ­moving machine learning beyond pattern recognition and natural language processing into a... Continue Reading →

Ethics Highlight ‘Day of Machine Learning Discussion’ | College of Computing

Machine learning at Georgia Tech was in the spotlight recently as The Center for Machine Learning at Georgia Tech (ML@GT) hosted its spring seminar on Feb. 22 in the Klaus Advanced Computing Building.Billed as a “day of discussions around machine learning,” more than 200 students and faculty from across campus registered for the daylong event.“AI is... Continue Reading →

Embodied Question Answering

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").

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 →

Create a website or blog at WordPress.com

Up ↑

%d bloggers like this: