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Yewen Xu
Researcher at Peking University
Publications - 4
Citations - 580
Yewen Xu is an academic researcher from Peking University. The author has contributed to research in topics: Feature (machine learning) & Computer science. The author has an hindex of 2, co-authored 2 publications receiving 198 citations.
Papers
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Proceedings ArticleDOI
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
TL;DR: An effective and efficient method called the AutoInt to automatically learn the high-order feature interactions of input features and map both the numerical and categorical features into the same low-dimensional space is proposed.
Proceedings ArticleDOI
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
TL;DR: In this article, a multi-head self-attentive neural network with residual connections is proposed to explicitly model the feature interactions in the low-dimensional space, which can be applied to both numerical and categorical input features.
Posted ContentDOI
MCIBox: A Toolkit for Single-molecule Multi-way Chromatin Interaction Visualization and Micro-Domains Identification
Simon Zhongyuan Tian,Guoliang Li,Duo Ning,Kai Jing,Yewen Xu,Yang Yang,Melissa J. Fullwood,Pengfei Yin,Guangyu Huang,Dariusz Plewczynski,Jixian Zhai,Ziwei Dai,Wei Chen,Meizhen Zheng +13 more
TL;DR: MCIBox is a toolkit for Multi-way Chromatin Interaction (MCI) analysis, including a visualization tool and a platform for identifying micro-domains with clustered single-molecule chromatin complexes, based on various clustering algorithms integrated with dimensionality reduction methods.
Journal ArticleDOI
MCI-frcnn: A deep learning method for topological micro-domain boundary detection
Simon Zhongyuan Tian,Pengfei Yin,Kai Jing,Yang Yang,Yewen Xu,Guangyu Huang,Duo Ning,Melissa J. Fullwood,Meizhen Zheng +8 more
TL;DR: Zhang et al. as mentioned in this paper developed the MCI-frcnn deep learning method to train a Faster Region-based Convolutional Neural Network (Faster R-CNN) for micro-domain boundary detection.