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
A fundamental question in Natural Language Processing (NLP) is how to represent words. If we have a paragraph we want to translate, or a product review we want to determine whether is positive or negative, or a question we want to answer, ultimately the easiest building block to start from is the individual word. The… Continue reading Learning to Represent Words by how They’re Spelled
Everyday skills, such as making your bed or even pressing a doorbell, might seem trivial to us, but are actually quite complicated for today’s robots. Think about your performance the first time you tried a sport. Did you seek help from a peer or coach? Did you perform better after that? Most probably you answered yes. It… Continue reading Robust Skill Generalization Using Probabilistic Inference
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 Visualizing Deep Learning Models at Facebook
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