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
At the start of the 2020 fall term, Wei “Coco” Xu, Alan Ritter, Shihao Yang, and Jing Li will join the Machine Learning Center at Georgia Tech (ML@GT) as faculty members. Xu and Ritter join as the center continues to expand its footprint in the natural language processing field. “We are happy and excited to… Continue reading ML@GT Expands Natural Language Processing and Data Science Research with New Faculty Hires
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 Erik Jorgensen,… Continue reading Meet ML@GT: Erik Jorgensen Develops Machine Learning Tools to Understand Malware
For the second time this summer, Frank Dellaert has been awarded a Test of Time Award. This time, the award comes from Robotics Science and Systems (RSS) and recognizes Dellaert’s papers, Square Root SAM and Square Root SAM: Simultaneous localization and mapping via square root information smoothing. The latter is co-authored by Michael Kaess, an… Continue reading Dellaert Wins Test of Time Award for a Second Time This Year
The International Conference on Machine Learning (ICML) received nearly 5,000 submissions for its 2020 conference and accepted 1,088 papers. Machine Learning Center at Georgia Tech (ML@GT) researchers authored nine accepted papers. The papers explore topics like privacy, semantics in predictive agents, data science, and artificial intelligence. One paper, Boosting Frank-Wolfe by Chasing Gradients, proposes a new state-of-the-art algorithm for constrained… Continue reading ML@GT to Present Nine Papers at Competitive Machine Learning Conference
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 Charles Lehman,… Continue reading Meet ML@GT: Charles Lehman Transitions from Navy Engineer to Machine Learning Ph.D. Student
Policy makers have long been wanting to improve infrastructure needed for the adoption of electric vehicles (EV), but with mass amounts of unstructured data they have been unable to determine how charging stations are performing and where more need to be added, according to a recent study from Georgia Institute of Technology researchers. Researchers in… Continue reading New Study Explores Sentiment Around Electric Vehicles, Leading to Faster Government Response and More Infrastructure
