no code implementations • 11 Nov 2022 • Yunpeng Zhao, Fugen Zhou, Bin Guo, Bo Liu
The proposed spatial temporal graph convolution block directly exploits BOLD time series as input features, which provides an interesting view for rsfMRI-based preclinical AD diagnosis.
no code implementations • 16 Jun 2022 • Yunpeng Zhao, Ning Hao, Ji Zhu
Biclustering on bipartite graphs is an unsupervised learning task that simultaneously clusters the two types of objects in the graph, for example, users and movies in a movie review dataset.
no code implementations • 21 Jun 2021 • Yefeng Wang, Yunpeng Zhao, Jiang Bian, Rui Zhang
We chose the best performing models in each task to assemble an end-to-end deep learning pipeline to detect DS AE signals and compared the results to the known DS AEs from a DS knowledge base (i. e., iDISK).
no code implementations • 21 Apr 2020 • Yunpeng Zhao, Peter Bickel, Charles Weko
Identifiability of parameters is a notoriously difficult problem for Bernoulli mixture models.
no code implementations • 26 Mar 2020 • Yunpeng Zhao, Mattia Prosperi, Tianchen Lyu, Yi Guo, Jiang Bian
Results show that crowdsourcing is useful to create high-quality annotations and active learning helps in reducing the number of required tweets, although there was no substantial difference among the strategies tested.
no code implementations • 22 May 2019 • Francois Modave, Yunpeng Zhao, Janice Krieger, Zhe He, Yi Guo, Jinhai Huo, Mattia Prosperi, Jiang Bian
Among American women, the rate of breast cancer is only second to lung cancer.
no code implementations • 7 Sep 2018 • Yunpeng Zhao, Qing Pan, Chengan Du
When searching for gene pathways leading to specific disease outcomes, additional information on gene characteristics is often available that may facilitate to differentiate genes related to the disease from irrelevant background when connections involving both types of genes are observed and their relationships to the disease are unknown.
no code implementations • 25 Aug 2018 • Yunpeng Zhao
In this article, we generalize the idea of the hub model into the case of grouped observations with temporal dependence.
no code implementations • 17 Mar 2017 • Chengan Du, Yunpeng Zhao, Feng Wang
We prove the consistency of graph-based learning in the case that the estimated scores are enforced to be equal to the observed responses for the labeled data.