Category: ICLR

Researchers from the Machine Learning Center at Georgia Tech (ML@GT) will present seven papers at the Ninth Annual International Conference on Learning Representations (ICLR). Accepted research will present new findings on topics such as increasing multi-platform deployment and semi-supervised object detection. [RELATED STORY: New Model Defense Strategy Prevents Cloning] ML@GT Associate Director and School of… Continue reading Machine Learning to Present Seven Papers at Prestigious Deep Learning Conference →

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 →

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 →

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 →