Richard Zemel’s research focuses on various aspects of machine learning, particularly unsupervised learning, boosting, and probabilistic networks. He studies applications such as image segmentation and collaborative filtering. He is also interested in neural coding, and has spent quite a bit of time working in the areas of perceptual learning, motion perception and visual attention.
New Opportunities Award, Canada Foundation for Innovation, 2002-2007.
Young Investigator Award, Office of Naval Research, 1998-2002.
National Sciences and Engineering Research Council Postgraduate Scholarship, 1989-1991.
Discovery Accelerator Supplement, National Sciences and Engineering Research Council
K. Xu et al, "Show, Attend and Tell: Neural Image Caption Generation with Visual Attention," ICML-2015: The 32nd International Conference on Machine Learning, 2015.
Senior Fellow Learning in Machines & Brains
University of TorontoDepartment of Computer Science
PhD (Computer Science) University of Toronto
MSc (Computer Science) University of Toronto
BA (History and Science) Harvard University
Ideas Related to Richard Zemel
Caption generation is a fundamental problem of artificial intelligence, one that distinguishes human intelligence – our ability to construct descriptions...
Breakthroughs in machine translation using neural networks have improved our ability to translate words and sentences between many languages. Researchers...
CIFAR fellows have created a machine learning system that generates captions for images from scratch, scanning scenes and putting together...