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Eric P. Xing
Researcher at Carnegie Mellon University
Publications - 725
Citations - 48035
Eric P. Xing is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Inference & Topic model. The author has an hindex of 99, co-authored 711 publications receiving 41467 citations. Previous affiliations of Eric P. Xing include Microsoft & Intel.
Papers
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Patent
System and method for processing a video stream to extract highlights
Eric P. Xing,Bin Zhou +1 more
TL;DR: In this article, the authors proposed a method of online video highlighting, a principled way of generating a short video highlight summarizing the most important and interesting contents of a potentially very long video, which is costly both time wise and financially for manual processing.
Proceedings ArticleDOI
Bilingual Word Spectral Clustering for Statistical Machine Translation
TL;DR: A variant of a spectral clustering algorithm is proposed for bilingual word clustering that generates the two sets of clusters for both languages efficiently with high semantic correlation within monolingual clusters, and high translation quality across the clusters between two languages.
Posted Content
CryptGraph: Privacy Preserving Graph Analytics on Encrypted Graph.
Pengtao Xie,Eric P. Xing +1 more
TL;DR: This paper presents how to encrypt a graph using homomorphic encryption and how to query the structure of an encrypted graph by computing polynomials, and proposes hard computation outsourcing to seek help from users to solve the problem that certain operations are not executable on encrypted graphs.
Proceedings ArticleDOI
Visualizing brand associations from web community photos
Gunhee Kim,Eric P. Xing +1 more
TL;DR: This paper proposes to go beyond text data and leverage large-scale online photo collections contributed by the general public and demonstrates that its approach can discover complementary views on the brand associations that are hardly mined from the text data.
Proceedings ArticleDOI
GenAMap: Visualization strategies for structured association mapping
TL;DR: A novel visual analytics system that replaces the time-consuming analysis of large-scale association mapping studies with exploratory visualization tools that give geneticists an overview of the data and lead them to relevant information called GenAMap.