J
Jingrui He
Researcher at University of Illinois at Urbana–Champaign
Publications - 177
Citations - 3918
Jingrui He is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Graph (abstract data type) & Multi-task learning. The author has an hindex of 29, co-authored 174 publications receiving 3179 citations. Previous affiliations of Jingrui He include Ningxia University & Carnegie Mellon University.
Papers
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Proceedings ArticleDOI
Manifold-ranking based image retrieval
TL;DR: MRBIR first makes use of a manifold ranking algorithm to explore the relationship among all the data points in the feature space, and then measures relevance between the query and all the images in the database accordingly, which is different from traditional similarity metrics based on pair-wise distance.
Journal ArticleDOI
Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data
TL;DR: This article compares the performance of Random Forests with three versions of logistic regression, and finds that the algorithmic approach provides significantly more accurate predictions of civil war onset in out-of-sample data than any of theLogistic regression models.
Book ChapterDOI
Classification of digital photos taken by photographers or home users
TL;DR: This paper addresses a specific image classification task, i.e. to group images according to whether they were taken by photographers or home users, and shows an application in No-Reference holistic quality assessment as a natural extension of such image classification.
Proceedings ArticleDOI
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification
Jun Wu,Jingrui He,Jiejun Xu +2 more
TL;DR: A generic degree-specific graph neural network named DEMO-Net motivated by Weisfeiler-Lehman graph isomorphism test that recursively identifies 1-hop neighborhood structures is proposed and two multi-task learning methods: degree- specific weight and hashing functions for graph convolution are designed.
Proceedings Article
A Graph-based Framework for Multi-Task Multi-View Learning
Jingrui He,Rick Lawrence +1 more
TL;DR: This paper introduces Multi-Task Multi-View (M2TV) learning for such complicated learning problems with both feature heterogeneity and task heterogeneity, and proposes a graph-based framework (GraM2) to take full advantage of the dual-heterogeneous nature.