Working Towards Explainable and Data-efficient Machine Learning Models via Symbolic Reasoning

By Yuan Yang In recent years, we have experienced the success of modern machine learning (ML) models. Many of them have led to unprecedented breakthroughs in a wide range of applications, such as AlphaGo beating a world champion human player or the introduction of autonomous vehicles. There has been a continuous effort, both from industry … Continue reading Working Towards Explainable and Data-efficient Machine Learning Models via Symbolic Reasoning

Explaining Machine Learning Models for Natural Language

By Sarah Wiegreffe and Yuval Pinter Natural language processing (NLP) is the study of how computers learn to represent and make decisions about human communication in the form of written text. This encompasses many tasks, including automatically classifying documents, using machines to translate between languages, or designing algorithms for writing creative stories.  Many state-of-the-art systems … Continue reading Explaining Machine Learning Models for Natural Language

Meet ML@GT: Cusuh Ham, a World Traveler Focused on Understanding Uncertainty in Machine Learning

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 Cusuh Ham, … Continue reading Meet ML@GT: Cusuh Ham, a World Traveler Focused on Understanding Uncertainty in Machine Learning

Escaping Saddle Points Faster with Stochastic Momentum

By Jun-Kun Wang, Chi-Heng Lin, and Jacob Abernethy SGD with stochastic momentum (see Figure 1 below) has been the de facto training algorithm in nonconvex optimization and deep learning. It has been widely adopted for training neural nets in various applications. Modern techniques in computer vision (e.g.[1,2]), speech recognition (e.g. [3]), natural language processing (e.g. … Continue reading Escaping Saddle Points Faster with Stochastic Momentum

Meet ML@GT: Abhishek Das Wants to Stop Climate Change and Develop AI Agents with Human-Level Skillsets

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 Abhishek Das, a … Continue reading Meet ML@GT: Abhishek Das Wants to Stop Climate Change and Develop AI Agents with Human-Level Skillsets

Accenture to Bring Their Tech Symposium to the Machine Learning Center at Georgia Tech

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

Learning to Cooperate in Multi-Agent Environments

By Jiachen Yang Over the years, human intelligence has evolved to work within a shared environment with other humans to do more than play Atari games or solve Rubik’s cubes alone in our rooms. The presence of other people demands our ability to handle a wide spectrum of complex interactions — we cooperate with colleagues … Continue reading Learning to Cooperate in Multi-Agent Environments