Georgia Tech Researchers Presenting Work Virtually at Top AI Conference Due to COVID-19

Due to the rapid spread of coronavirus (COVID-19) and resulting travel restrictions, Georgia Tech students and faculty will now be presenting their research virtually at the International Conference on Learning Representations (ICLR), one of the biggest artificial intelligence (AI) conferences in the world, April 25 through 30. With 17 papers to present, researchers will create a … Continue reading Georgia Tech Researchers Presenting Work Virtually at Top AI Conference Due to COVID-19

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

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

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

ICLR 2018 accepted papers and ML@GT

The list of accepted papers at ICLR 2018 was released last week and Machine Learning at Georgia Tech (ML@GT) had a strong presence. Out of 935 submissions, 23 oral and 314 conference papers were accepted (roughly 36%). We are pleased to announce that Georgia Tech had 10 conference papers this year, with 1 of them … Continue reading ICLR 2018 accepted papers and ML@GT