M
Mugizi Robert Rwebangira
Researcher at Howard University
Publications - 18
Citations - 516
Mugizi Robert Rwebangira is an academic researcher from Howard University. The author has contributed to research in topics: Graph (abstract data type) & Semi-supervised learning. The author has an hindex of 6, co-authored 18 publications receiving 488 citations. Previous affiliations of Mugizi Robert Rwebangira include Carnegie Mellon University.
Papers
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Proceedings ArticleDOI
Semi-supervised learning using randomized mincuts
TL;DR: The experiments on several datasets show that when the structure of the graph supports small cuts, this can result in highly accurate classifiers with good accuracy/coverage tradeoffs, and can be given theoretical justification from both a Markov random field perspective and from sample complexity considerations.
Person Identification in Webcam Images: An Application of Semi-Supervised Learning
Maria-Florina Balcan,Avrim Blum,Patrick Pakyan Choi,John Lafferty,Brian Pantano,Mugizi Robert Rwebangira,Xiaojin Zhu +6 more
TL;DR: The person identification task is posed as a graph-based semi-supervised learning problem, where only a few training images are labeled and the importance of domain knowledge in graph construction is discussed, and experiments are presented that clearly show the advantage of semi- supervised learning over standard supervised learning.
Proceedings Article
A random-surfer web-graph model
TL;DR: In this paper, theoretical and experimental results on a random-surfer model for construction of a random graph are provided, showing that in certain formulations, this results in the same distribution as the preferential-attachment random-graph model.
Journal ArticleDOI
Exploring a graph theory based algorithm for automated identification and characterization of large mesoscale convective systems in satellite datasets
K. D. Whitehall,K. D. Whitehall,Chris A. Mattmann,Chris A. Mattmann,Gregory S. Jenkins,Mugizi Robert Rwebangira,Belay Demoz,Duane E. Waliser,Duane E. Waliser,Jinwon Kim,C. E. Goodale,Andrew F. Hart,Paul Ramirez,Michael J. Joyce,Maziyar Boustani,Paul Zimdars,Paul C. Loikith,Huikyo Lee +17 more
TL;DR: The results show that applying graph theory to this problem allows for the identification of features from infrared satellite data and the seamlessly identification in a precipitation rate satellite-based dataset, while innately handling the inherent complexity and non-linearity of mesoscale convective systems.
Journal ArticleDOI
Intensity-Based Skeletonization of CryoEM Gray-Scale Images Using a True Segmentation-Free Algorithm
TL;DR: This paper presents a segmentation-free approach to extract the gray-scale curve-like skeletons of cryo-electron microscopy images, which relies on a novel representation of the 3D image, where the image is modeled as a graph and a set of volume trees.