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
Recap: ML@GT Fall Seminar Series
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
Convergence of Value Aggregation for Imitation Learning
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
From Object Interactions to Fine-grained Video Understanding
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
Learning to Represent Words by how They’re Spelled
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
Robust Skill Generalization Using Probabilistic Inference
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
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 Visualizing Deep Learning Models at Facebook
