Wednesday, January 19, 2022 - 1:00pm to 2:00pm

Speaker Information

Stephen Gould
Computer Science
Australian National University


Deep declarative networks (DDNs) are a new class of deep learning model that allows optimization problems to be embedded within end-to-end learnable pipelines. In this talk I will introduce DDNs and related concepts---implicit layers and differentiable optimization---and give some formal results for second-order differentiable problems. I will then present a concrete example of a DDN layer in the case of optimal transport, and show that by applying the DDN results we can obtain significant memory and speed improvements over unrolling Sinkhorn iterates, as would be required in traditional deep learning models. Finally, I demonstrate an end-to-end model for solving the blind perspective-n-point problem that makes use of two DDN layers. Limitations of DDNs and open questions will also be discussed.

Speaker Bio

Stephen Gould is a Professor of Computer Science at the Australian National University (ANU). He is also an Australian Research Council (ARC) Future Fellow and Amazon Scholar. He is a former ARC Postdoctoral Fellow, Microsoft Faculty Fellow, Contributed Researcher at Data61, Principal Research Scientist at Amazon Inc, and Director of the ARC Centre of Excellence in Robotic Vision. Stephen received his BSc degree in mathematics and computer science and BE degree in electrical engineering from the University of Sydney in 1994 and 1996, respectively. He received his MS degree in electrical engineering from Stanford University in 1998. He then worked in industry for several years where he co-founded Sensory Networks, which later sold to Intel in 2013. In 2005 he returned to Stanford University and was awarded his PhD degree in 2010. In November 2010, he moved back to Australia to take up a faculty position at the ANU. Stephen has broad interests in the areas of computer and robotic vision, machine learning, deep learning, structured prediction, and optimization. He teaches courses on advanced machine learning, research methods in computer science, and the craft of computing. His main research focus is on automatic semantic, dynamic and geometric understanding of images and videos.