Autoencoder ae - Open Access .click

Arabian Journal for Science and Engineering, 2019
Recommender systems enable users to deal with the information overload problem by serving personalized predictions. Traditional recommendation techniques produce referrals for users by considering their overall opinions over items. On the other hand, users may consider several criteria while evaluating an item.
Zeynep Batmaz, Cihan Kaleli
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AE-DCNN: Autoencoder Enhanced Deep Convolutional Neural Network For Malware Classification

2021 International Conference on Intelligent Technologies (CONIT), 2021
Malware classification is a problem of great significance in the domain of information security. This is because the classification of malware into respective families helps in determining their intent, activity, and level of threat. In this paper, we propose a novel deep learning approach to malware classification. The proposed method converts malware
Shashank Kumar +3 more
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PM-AE: Pyramid Memory Autoencoder for Unsupervised Textured Surface Defect Detection

2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), 2020
Anomaly detection for textured surface is a key task in product quality control. In recent years, supervised deep learning approaches have begun to be applied in this field, whereas most of the approaches are usually impracticable in collecting a large scale of defect samples. To this end, this paper proposes an efficient pyramid memory autoencoder.
Haiming Yao, Dan Li, Yiwen Zhu, Wenyong Yu +3 more
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autoencoder
deep learning
anomaly detection

machine learning
autoencoders
extreme learning machine

unsupervised learning