The 2022 cohort for the J.P. Morgan AI PhD Fellowships Program includes Pranav Shetty and Zijie “Jay” Wang, both students in the Machine Learning PhD program at Georgia Tech. The institute is the only university with multiple recipients among the new group of 11. Both students are advised by College of Computing faculty. The J.P.… Continue reading Georgia Tech Machine Learning Students Earn J.P. Morgan AI PhD Fellowships
Georgia Tech and Meta have launched a new co-teaching initiative, the Artificial Intelligence Learning Alliance (AILA), with the goal of bringing graduate-level machine learning educational opportunities to historically black colleges and universities (HBCUs) and other minority-serving institutions (MSIs). For context, only 3.8 percent of computing Ph.D. graduates in 2020 were Black, Hispanic, or Native American,… Continue reading Partnership with Meta Brings New Online Machine Learning Resources to HBCUs
Georgia Tech researchers and their collaborators have new published research in the Thirty-Sixth AAAI Conference on Artificial Intelligence which starts this week. The conference, Feb. 22 – March 1, is the main annual event for the Association for the Advancement of Artificial Intelligence and aims to promote research in, and responsible use of, artificial intelligence.… Continue reading Georgia Tech at AAAI-22
Over the years, the Machine Learning Center at Georgia Tech (ML@GT) has steadily been increasing its presence in the natural language processing (NLP) community. With a few new hires this fall and a big performance at this year’s Conference on Empirical Methods in Natural Language Processing (EMNLP), ML@GT is becoming a major player in the field. With 10 papers accepted… Continue reading ML@GT Further Establishes Itself in Natural Language Processing Community
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
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
By Allie McFadden, Communications Officer In an effort to expand services in machine learning, artificial intelligence (AI), and data science, Accenture will hold a Tech Symposium on Feb. 25 at the Machine Learning Center at Georgia Tech (ML@GT.) During the three-day event, guests will be treated to a tour of ML@GT’s new home in Coda… Continue reading Accenture to Bring Their Tech Symposium to the Machine Learning Center at Georgia Tech
Throughout the fall semester, the Machine Learning Center at Georgia Tech was fortunate to bring world-class researchers, academics, and industry professionals to Georgia Tech's campus as a part of our Fall Seminar Series. From using machine learning in retail strategy to few-shot learning to understanding the limitations of artificial intelligence, our guest speakers inspired our… Continue reading Recap: ML@GT Fall Seminar Series
The following describes work by Ching-An Cheng and Byron Boots, which was awarded Best Paper at the Further details and proofs are available at The 21st International Conference on Artificial Intelligence and Statistics (AISTATS). The paper can be found here: https://arxiv.org/abs/1801.07292. Learning to make sequential decisions is a fundamental topic in designing automatic agents with artificial intelligence.… Continue reading Convergence of Value Aggregation for Imitation Learning
Video understanding tasks such as action recognition and caption generation are crucial for various real-world applications in surveillance, video retrieval, human behavior understanding, etc. In this work, we present a generic recurrent module to detect relationships and interactions between arbitrary object groups for fine-grained video understanding. Our work is applicable to various open domain video… Continue reading From Object Interactions to Fine-grained Video Understanding
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