Machine Learning Center at Georgia Tech (ML@GT) and School of Computational Science and Engineering (CSE) Ph.D. student Xinshi Chen is being recognized for her work in machine learning with a prestigious fellowship. Chen specializes in principled machine learning research with a focus on learning-based algorithm design and deep learning structured data. Her work has garnered the attention… Continue reading Google Awards ML@GT Student for Outstanding Machine Learning Research
This month, The Machine Learning Center at Georgia Tech (ML@GT) is co-hosting a live, virtual event featuring the New York Times Research and Development (R&D) team. Set for Oct. 30 in collaboration with the University of California, Berkeley, members of the R&D team will participate in a virtual conversation to discuss how the Times is… Continue reading NYT R&D Team to Discuss Technology’s Impact on Journalism in Live, Virtual Event
The Machine Learning Center at Georgia Tech (ML@GT) is home to many talented students from across campus, representing all six of Georgia Tech’s colleges and the Georgia Tech Research Institute (GTRI). These students have diverse backgrounds and a wide variety of interests both inside and outside of the classroom. Today, we’d like you to meet Andrew Sedler, a… Continue reading Meet ML@GT: Andrew Sedler on Improving Neuroprosthetic Devices
A mathematician by trade, Molei Tao, typically uses mathematics to design algorithms and solve physical science problems like how planets move. Recently, he became attracted to machine learning, an area that according to him, contains numerous interesting problems that are mathematically exciting and can benefit from modern mathematical tools. This year, Tao published his first… Continue reading Tao Wins Best Paper Award at Artificial Intelligence and Statistics Conference
Welcome to Learning Machines, where we’ll talk with faculty members from the Machine Learning Center at Georgia Tech (ML@GT) about their main research area and the future of their work. Today we talked with Diyi Yang, an assistant professor in ML@GT and the School of Interactive Computing. Yang’s lab, Social and Language Technologies (SALT), combines… Continue reading Learning Machines: Natural Language Processing Explained with Diyi Yang
The Machine Learning Center at Georgia Tech (ML@GT) is home to many talented students from across campus, representing all six of Georgia Tech’s colleges and the Georgia Tech Research Institute (GTRI). These students have diverse backgrounds and a wide variety of interests both inside and outside of the classroom. Today, we’d like you to meet Yuan Yang, a… Continue reading Meet ML@GT: Yuan Yang Looks to Find the ‘Right’ Problem to Solve
Summertime is usually a time when students from the Machine Learning Center at Georgia Tech (ML@GT) are dispersed around the world interning at prestigious companies. While a global pandemic kept them from working out of shiny corporate offices and labs, however, students were able to still remotely intern for companies like Facebook, Qualcomm, Microsoft Research,… Continue reading ML@GT Students Learn Valuable Skills from Internships Despite Participating Remotely
This year’s European Conference on Computer Vision (ECCV) showcases 1,360 papers – 15 of them from the Machine Learning Center at Georgia Tech (ML@GT.) The papers cover a vast array of topics including an idea on how to improve vision and language navigation and a new model that is learning to generate grounded visual captions… Continue reading ML@GT Makes a Strong Showing at Premier European Computer Vision Conference
Welcome to Learning Machines, where we’ll talk with faculty members from the Machine Learning Center at Georgia Tech (ML@GT) about their main research area and the future of their work. Today we talked with Polo Chau, the associate director for corporate relations at ML@GT, an associate professor in the School of Computational Science and Engineering,… Continue reading Learning Machines: Polo Chau Explains Data Visualizations
Annotating and labeling datasets for machine learning problems is an expensive and time-consuming process for computer vision and natural language scientists. However, a new deep learning approach is being used to decode, localize, and reconstruct image and video captions in seconds, making the machine-generated captions more reliable and trustworthy. To solve this problem, researchers at… Continue reading New Algorithm Follows Human Intuition to Make Visual Captioning More Grounded
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