From Flat To Depth: A Learning Journey
Deep learning is the latest in a series of paradigm-shifting advances in artificial intelligence that have captured the public attention. This talk details our migration towards connectionist and deep learning models in the course of our research on the detection of retinal disease conditions, and explores the potential of melding deep learning with classical computer vision and image processing techniques.
Gilbert is currently a Research Fellow with iLab, and has worked extensively in the computer vision and machine learning domains to develop reliable methods for disease classification. This has inevitably resulted in the discovery of many approaches that don't quite work, and fortunately, some that seem to do so. He holds a Ph.D. in Computer Science and bachelor's degrees in Computer Science and Economics, all from the National University of Singapore.