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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Unsupervised Anomaly Detection Using Variational Auto-Encoder based Feature Extraction
Rong Yao, Chongdang Liu, Linxuan Zhang, et al.
(2019), pp. 1-7
Closed Access | Times Cited: 67

Showing 51-75 of 67 citing articles:

Detection of mobile network abnormality using deep learning models on massive network measurement data
Bing Qian, Shun Lu
Computer Networks (2021) Vol. 201, pp. 108571-108571
Closed Access | Times Cited: 2
Build Connections Between Two Groups of Images Using Deep Learning Method
Hongfei Du, Emre Barut, Juntao Su
2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) (2022), pp. 1231-1236
Closed Access | Times Cited: 1
Advances in Electron Microscopy with Deep Learning
Jeffrey M. Ede
arXiv (Cornell University) (2021)
Open Access | Times Cited: 1
Harnessing the Power of Generative Models for Mobile Continuous and Implicit Authentication
Ezzeldin Tahoun
(2021)
Closed Access | Times Cited: 1
Cross-Modal Knowledge Distillation for Fault Detection under Multiple Failure Modes
Jinghao Zheng, Chongdang Liu, Linxuan Zhang
2021 China Automation Congress (CAC) (2021) Vol. 10, pp. 7714-7719
Closed Access | Times Cited: 1
Open-source tool for model performance analysis for subpopulations
Stijn Vandewiele, Jonas De Vylder, Bart Diricx, et al.
Medical Imaging 2018: Computer-Aided Diagnosis (2023), pp. 70-70
Closed Access
Intelligent Unsupervised Network Traffic Classification Method Using Adversarial Training and Deep Clustering for Secure Internet of Things
Weijie Zhang, Lanping Zhang, Xixi Zhang, et al.
Future Internet (2023) Vol. 15, Iss. 9, pp. 298-298
Open Access
LSCR: Latent Space Coordination Relation for Anomaly Prediction
Thasorn Chalongvorachai, Kuntpong Woraratpanya
(2023), pp. 13-18
Closed Access
An Unsupervised Representation Learning Method for MTC Using Masked Autoencoder
Ke Xu, Xixi Zhang, Shennan Huang, et al.
(2023), pp. 1313-1317
Closed Access
Controllability-Constrained Deep Neural Network Models for Enhanced Control of Dynamical Systems
Suruchi Sharma
(2023)
Open Access
Performance and Compliance Anomaly Detection
Gabriel Iuhasz
(2023), pp. 20-25
Closed Access
Structured Deep Learning: Theory and Applications
Fangqi Zhu
(2020)
Closed Access
Comparison of Anomaly Detectors: Context Matters
Hans Skvara, Jan Franců, Matěj Zorek, et al.
arXiv (Cornell University) (2020)
Closed Access
MCVAE: Multi-channel Variational Autoencoder for Anomaly Detection
Wanzhen Zhang, Liheng Xu, Zhihui Yu, et al.
(2022), pp. 1-6
Closed Access
Deep Contrastive Anomaly Detection for Airline Ancillaries Prediction
Pu Yang, Arinbjörn Kolbeinsson, Naman Shukla, et al.
(2022), pp. 1167-1174
Closed Access
Unsupervised heart abnormality detection based on phonocardiogram analysis with Beta Variational Auto-Encoders
Shengchen Li, Ke Tian, Rui Wang
arXiv (Cornell University) (2021)
Open Access
Automatically detecting anomalous exoplanet transits.
Christoph J. Hönes, B. Miller, A. M. Heras, et al.
arXiv (Cornell University) (2021)
Closed Access
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