Suchergebnisse - Sparse autoencoder - stacked contractive autoencoder

Network intrusion detection based on Contractive Sparse Stacked Denoising Autoencoder von Lu, Jizhao, Meng, Huiping, Li, Wencui, Liu, Yue, Guo, Yihao, Yang, Yang

“… identify and classify network intrusion data, this paper proposes a Contractive Sparse Stack Denoising Autoencoder(CSSDAE …”
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Hybrid Network Intrusion Detection with Stacked Sparse Contractive Autoencoders and Attention-based Bidirectional LSTM von Bi, Jing, Guan, Ziyue, Yuan, Haitao

“… SABD integrates Stacked sparse contractive autoencoders, Attention-based Bidirectional long-term and short-term memory (LSTM …”
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Improved network intrusion classification with attention-assisted bidirectional LSTM and optimized sparse contractive autoencoders von Bi, Jing, Guan, Ziyue, Yuan, Haitao, Zhang, Jia

“… SABD integrates Stacked sparse contractive autoencoders (SSCA), Attention-based Bidirectional long-term and short-term memory (LSTM …”
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Enhanced fault detection in digital VLSI circuits using convolutional autoencoders von Savalam, Chandrasekhar, Medisetti, Sanjay, Korapati, Prasanti

“… A Convolutional Autoencoder (CAE) is employed to extract spatial and structural features from circuit test patterns, effectively reducing dimensionality while preserving fault-related information …”
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An Overview of Unsupervised Deep Feature Representation for Text Categorization von Wang, Shiping, Cai, Jinyu, Lin, Qihao, Guo, Wenzhong

“… High-dimensional features are extensively accessible in machine learning and computer vision areas. How to learn an efficient feature representation for …”
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Deep-Learning-Enabled Security Issues in the Internet of Things von Lv, Zhihan, Qiao, Liang, Li, Jinhua, Song, Houbing

“… ), stacked contractive autoencoder (SCAE), stacked sparse autoencoder (SSAE), deep belief network (DBN …”
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Application of deep autoencoder as an one-class classifier for unsupervised network intrusion detection: a comparative evaluation von Vaiyapuri, Thavavel, Binbusayyis, Adel

“… Intuitively, unsupervised deep learning approaches has received gaining momentum. Specifically, the advances in deep learning has endowed autoencoder (AE …”
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Sparsely connected autoencoder von Gupta, Kavya, Majumdar, Angshul

“… For classification we have compared against stacked autneencoders, contractive autoencoders, deep belief network, sparse deep neural network and optimal brain damage neural network …”
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An Intelligent Fault Diagnosis Method of Multi-Scale Deep Feature Fusion Based on Information Entropy von Shang, Zhiwu, Li, Wanxiang, Gao, Maosheng, Liu, Xia, Yu, Yan

“… First, a normal autoencoder, denoising autoencoder, sparse autoencoder, and contractive autoencoder are used in parallel to construct a multi-scale deep neural network feature extraction structure …”
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Blockchain and Deep Learning Empowered Secure Data Sharing Framework for Softwarized UAVs von Kumar, Prabhat, Kumar, Randhir, Kumar, Abhinav, Franklin, A. Antony, Jolfaei, Alireza

“… Softwarized Unmanned Aerial Vehicles (UAVs) use network programmability concept of Software-Defined Network (SDN) to separate the hardware control layer from …”
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Majorization Minimization Technique for Optimally Solving Deep Dictionary Learning von Singhal, Vanika, Majumdar, Angshul

“… The concept of deep dictionary learning (DDL) has been recently proposed. Unlike shallow dictionary learning which learns single level of dictionary to …”
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An Adaptive Service Classification Method Based on Stacked Hybrid Auto-Encoder von Li, Jing, Zhang, Zhi, Zhang, Tiankui

“… In this paper, we investigate the problem of communication services classification and propose an adaptive service classification method which is based on stacked hybrid autoencoder (SHAE …”
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A Novel Deep Feature Learning Method Based on the Fused-Stacked AEs for Planetary Gear Fault Diagnosis von Chen, Xihui, Ji, Aimin, Cheng, Gang

“… In this paper, a novel deep feature learning method based on the fused-stacked autoencoders (AEs) for planetary gear fault diagnosis was proposed …”
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Majorization Minimization Technique for Optimally Solving Deep Dictionary Learning von Singhal, Vanika, Majumdar, Angshul

“… The concept of deep dictionary learning has been recently proposed. Unlike shallow dictionary learning which learns single level of dictionary to represent the …”
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