Thus, SpotLife looked to define a new category in this space, challenging people to change the way they communicated, driving market adoption of Personal Video Broadcasting. While web cams and Internet broadcasting had been around for several years, it was a market space cluttered with voyeur-cams and video conferencing. In early May 2000, SpotLife launched its personal video broadcasting service that gave people the ability to broadcast live or stored audio and video content over the Internet. This event was successful in garnering local and national print and broadcast attention for the small Internet company, driving thousands of new users to the site.
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Graduates received the Internet cameras and SpotLife software with their diplomas during the graduation ceremony. The goal was to provide the students with a new way to maintain contact with their friends and family after high school. Wed.SpotLife, a small Silicon Valley start-up, joined with parent company Logitech in June 2000 to donate 400 SpotLife-enabled QuickCam Internet video cameras to a local high school’s graduating class. He has been invited to serve as general chair, program committee chair, local chair, or area chair for a dozen of top conferences including SIGIR, Related Events (a corresponding poster, oral, or spotlight)Ģ022 Spotlight: SE(3) Equivariant Graph Neural Networks with Complete Local Frames »
He has won quite a few awards, including the best student paper award at SIGIR (2008), the most cited paper award at Journal of Visual Communications and Image Representation (2004-2006), the research break-through award (2012) and research-team-of-the-year award (2017) at Microsoft Research, and Top-10 Springer Computer Science books by Chinese authors (2015), and the most cited Chinese researcher by Elsevier (2017). His papers have been cited for tens of thousands of times in refereed conferences and journals. He is an adjunct/honorary professor at Carnegie Mellon University (CMU), University of Nottingham, and several other universities in China. On the other hand, he has been actively contributing to academic communities. Many of his technologies have been transferred to Microsoft’s products and online services (such as Bing, Microsoft Advertising, and Azure), and open-sourced through Microsoft Cognitive Toolkit (CNTK), Microsoft Distributed Machine Learning Toolkit (DMTK), and Microsoft Graph Engine. He is very well known for his pioneer work on learning to rank and computational advertising, and his recent research interests include deep learning, reinforcement learning, and distributed machine learning. Tie-Yan Liu is a principal researcher of Microsoft Research Asia, leading the research on artificial intelligence and machine learning. Extensive experimental results demonstrate that our model achieves the best or competitive performance in two types of datasets. We evaluate our method on two tasks: Newton mechanics modeling and equilibrium molecule conformation generation. Since the frames are built only by cross product operations, our method is computationally efficient. The local frame is constructed to form an orthonormal basis that avoids direction degeneration and ensure completeness. In this paper, we propose a framework to construct SE(3) equivariant graph neural networks that can approximate the geometric quantities efficiently.Inspired by differential geometry and physics, we introduce equivariant local complete frames to graph neural networks, such that tensor information at given orders can be projected onto the frames.
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Therefore, how to better trade-off the expressiveness and computational efficiency plays a core role in the design of the equivariant deep learning models. Constructing an equivariant neural network generally brings high computational costs to ensure expressiveness. In light of this, great efforts have been put on encoding this symmetry into deep neural networks, which has been shown to improve the generalization performance and data efficiency for downstream tasks. It enables robust and accurate prediction under arbitrary reference transformations. SE(3) equivariance) is a critical physical symmetry in science, from classical and quantum physics to computational biology.
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