Toward building a cost-effective and robust machine learning system
Cuong Nguyen
Cuong Nguyen is an Assistant Professor of the Knight Foundation School of Computing and Information Sciences, Florida International University (FIU). Before joining FIU, he was an Applied Scientist at Amazon Web Services (AWS), working on machine learning with applications to computer vision and natural language processing. Before AWS, he was a postdoc at the Department of Industrial Systems Engineering, National University of Singapore, and then at the Department of Engineering, Cambridge University. He received Bachelor and PhD degrees in Computer Science from the School of Computing, National University of Singapore. His research interests include probabilistic machine learning and artificial intelligence.
Artificial intelligence and machine learning (ML) are changing the world. They are more and more a part of our lives, empowering smartphones, online shopping, business analytics, etc. The technology behind the success of modern ML, deep learning, although having excellent predictive power, often requires very high cost to develop, from collecting data to training and evaluating models. In this talk, we will discuss several aspects of building a cost-effective ML system, from using active learning to reduce data collection cost to applying continual and transfer learning to reduce model training cost. We will also focus on the problem of transferability estimation and model selection that can be used for efficiently selecting the best source model to transfer to a target task, thus enabling the development of an ML model for the target task using only a small amount of data and computation time.
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