Zoom: https://oregonstate.zoom.us/j/91611213801?pwd=Wm9JSkN1eW84RUpiS2JEd0E5T…
Equivariant representations are crucial in various scientific and engineering domains because they encode the inherent symmetries present in physical and biological systems, thereby providing a more natural and efficient way to model them. In the context of machine learning and perception, equivariant representations ensure that the output of a model changes in a predictable way in response to transformations of its input, such as 2D or 3D rotation or scaling. In this talk, we will show a systematic way of how to achieve equivariance by design and how such an approach can yield efficiency in training data and model capacity. We will present examples on spherical networks, equivariant representation for point clouds, and a novel definition of convolution and attention on lightfields.
Kostas Daniilidis is the Ruth Yalom Stone Professor of Computer and Information Science at the University of Pennsylvania where he has been faculty since 1998. He is an IEEE Fellow. He was the director of the GRASP laboratory from 2008 to 2013, Associate Dean for Graduate Education from 2012-2016, and Faculty Director of Online Learning from 2013- 2017. He obtained his undergraduate degree in Electrical Engineering from the National Technical University of Athens, 1986, and his PhD in Computer Science from the University of Karlsruhe, 1992, under the supervision of Hans-Hellmut Nagel. He received the Best Conference Paper Award at ICRA 2017. He co-chaired ECCV 2010 and 3DPVT 2006. His most cited works have been on event-based vision, equivariant learning, 3D human pose, and hand-eye calibration.