R
Rui Zhang
Researcher at National University of Singapore
Publications - 331
Citations - 7520
Rui Zhang is an academic researcher from National University of Singapore. The author has contributed to research in topics: Nearest neighbor search & k-nearest neighbors algorithm. The author has an hindex of 38, co-authored 314 publications receiving 5580 citations. Previous affiliations of Rui Zhang include AT&T & Northeastern University (China).
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Journal ArticleDOI
iDistance: An adaptive B+-tree based indexing method for nearest neighbor search
TL;DR: An efficient B+-tree based indexing method for K-nearest neighbor (KNN) search in a high-dimensional metric space, called iDistance, which partitions the data based on a space- or data-partitioning strategy, and selects a reference point for each partition.
Proceedings ArticleDOI
Destination prediction by sub-trajectory synthesis and privacy protection against such prediction
TL;DR: The privacy protection issue in case an adversary uses SubSyn algorithm to derive sensitive location information of users is considered, and an efficient algorithm to select a minimum number of locations a user has to hide on her trajectory in order to avoid privacy leak is proposed.
Journal ArticleDOI
Entity Alignment between Knowledge Graphs Using Attribute Embeddings
TL;DR: This work proposes to learn embeddings that can capture the similarity between entities in different knowledge graphs that achieves consistent improvements over the baseline models by over 50% in terms of hits@1 on the entity alignment task.
Proceedings Article
Identifying at-risk students in massive open online courses
TL;DR: This paper explores the accurate early identification of students who are at risk of not completing courses, and proposes two transfer learning algorithms to trade-off smoothness and accuracy by adding a regularization term to minimize the difference of failure probabilities between consecutive weeks.
Proceedings Article
KDGAN: Knowledge Distillation with Generative Adversarial Networks
TL;DR: A three-player game named KDGAN consisting of a classifier, a teacher, and a discriminator, where the classifier and the teacher learn from each other via distillation losses and are adversarially trained against the discriminator via adversarial losses.