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Wenyi Huang
Researcher at Pennsylvania State University
Publications - 15
Citations - 1265
Wenyi Huang is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Deep learning & Citation. The author has an hindex of 12, co-authored 15 publications receiving 1083 citations. Previous affiliations of Wenyi Huang include Penn State College of Information Sciences and Technology & Tsinghua University.
Papers
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Proceedings Article
Automatic Keyphrase Extraction via Topic Decomposition
TL;DR: A Topical PageRank (TPR) is built on word graph to measure word importance with respect to different topics and shows that TPR outperforms state-of-the-art keyphrase extraction methods on two datasets under various evaluation metrics.
Book ChapterDOI
MtNet: A Multi-Task Neural Network for Dynamic Malware Classification
Wenyi Huang,Jack W. Stokes +1 more
TL;DR: A new multi-task, deep learning architecture for malware classification for the binary i.e. malware versus benign malware classification task, which achieves a binary classification error rate of 0.358i¾?%, and for the first time, sees improvements using multiple layers in a deep neural network architecture for ransomware classification.
Proceedings Article
A neural probabilistic model for context based citation recommendation
TL;DR: A novel neural probabilistic model that jointly learns the semantic representations of citation contexts and cited papers is proposed that significantly outperforms other state-of-the-art models in recall, MAP, MRR, and nDCG.
Proceedings ArticleDOI
Recommending citations: translating papers into references
TL;DR: This work proposes a method that "translates" research papers into references by considering the citations and their contexts from existing papers as parallel data written in two different "languages" using the translation model to create a relationship between these two "vocabularies".
Proceedings ArticleDOI
Measuring Prerequisite Relations Among Concepts
TL;DR: This work investigates the problem of measuring prerequisite relations among concepts and proposes a simple link-based metric, namely reference distance (RefD), that effectively models the relation by measuring how differently two concepts refer to each other.