Search Results - Sparse autoencoder - stacked contractive autoencoder

Network intrusion detection based on Contractive Sparse Stacked Denoising Autoencoder by 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 by 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 by 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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Deep-Learning-Enabled Security Issues in the Internet of Things by Lv, Zhihan, Qiao, Liang, Li, Jinhua, Song, Houbing

“…), stacked contractive autoencoder (SCAE), stacked sparse autoencoder (SSAE), deep belief network (DBN…”
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An Overview of Unsupervised Deep Feature Representation for Text Categorization by 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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Application of deep autoencoder as an one-class classifier for unsupervised network intrusion detection: a comparative evaluation by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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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