Suchergebnisse - stacked variational autoencoder

SVDTI: Stacked variational autoencoder with SMILES-based drug representations for identifying drug-target interaction von Ha, Jihwan

“… approaches to predict potential DTIs efficiently. In this paper, we introduce a novel method, which employs a stacked variational autoencoder (SVAE …”
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A Variational Stacked Autoencoder with Harmony Search Optimizer for Valve Train Fault Diagnosis of Diesel Engine von Chen, Kun, Mao, Zhiwei, Zhao, Haipeng, Jiang, Zhinong, Zhang, Jinjie

“… To handle this issue, this paper proposed a variational stacked autoencoder (VSAE) to adaptively extract features from angular domain signals …”
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Text feature extraction based on stacked variational autoencoder von Che, Lei, Yang, Xiaoping, Wang, Liang

“… This paper presents a text feature extraction model based on stacked variational autoencoder (SVAE …”
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Semi-supervised Deep Learning in Motor Imagery-Based Brain-Computer Interfaces with Stacked Variational Autoencoder von Chen, Junjian, Yu, Zhuliang, Gu, Zhenghui

“… To address this problem, we propose a semi-supervised deep learning method based on the stacked variational autoencoder (SVAE …”
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SVAD: Stacked Variational Autoencoder Deep Neural Network-Based Dimensionality Reduction and Classification of Small Sample Size and High Dimensional Data von Srivastava, Neha, Tayal, Devendra K.

“… and over-fitting.To address these issues, we proposed a dynamic variational autoencoder based deep neural network architecture, based on a mathematical foundation for unsupervised learning …”
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A novel 3D indoor localization method integrating deep spatial feature augmentation and attention-based denoising von Liu, Chunyuan, Yu, Xiaomin, Wu, Peilong, Wang, Haoyu

“… Specifically, a stacked variational autoencoder …”
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Network Anomaly Detection With Stacked Sparse Shrink Variational Autoencoders and Unbalanced XGBoost von Bi, Jing, Guan, Ziyue, Yuan, Haitao, Yang, Jinhong, Zhang, Jia

“… In addition, the hyperparameter optimization of their models cannot be effectively solved. In this letter, based on the improvement of variational autoencoders, stacked sparse shrink variational autoencoders (S3VAEs) are designed …”
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SVAE‐GRU‐based degradation generation and prediction for small samples von Shangguan, Anqi, Feng, Nan, Mu, Lingxia, Fei, Rong, Hei, Xinhong, Xie, Guo

“… New degradation data are generated by combining the Stacked Variational Autoencoder (SVAE …”
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Soft Sensor Based on Fusion Modeling with the Balanced Spatio-temporal Feature Network von Tang, Xiaochu, Wang, Yiting, Tao, Na, Yu, Yang

“… or even submerged．In this paper, a stacked variational autoencoder based on balanced spatio－temporal feature network（BST－ SVAE …”
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Stacked Variational Autoencoder in the Classification of Cardiac Arrhythmia using ECG Signals with 2D-ECG Images von Nithya, S, Rani, M. Mary Shanthi

“… Prompt diagnosis of arrhythmia can reduce the mortality rate. In this paper, we have investigated the application of a Stacked Variational Autoencoder (SVAE …”
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Order Track Variational Stacked Autoencoder Fault Diagnosis Model for Complex Working Conditions von Yu, Tianyi, Li, Shunming, Lu, Jiantao, Gong, Siqi

“… In order to solve the above problems, a fault diagnosis model of order track variational stacked autoencoder (OTV-SAE …”
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Robust prediction of remaining useful lifetime of bearings using deep learning von Magadán, L., Granda, J.C., Suárez, F.J.

“… Predicting the remaining useful lifetime (RUL) of bearings in electric motors is crucial to reduce repair costs in industrial maintenance. With the …”
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Stacked Denoising Variational Auto Encoder Model for Extractive Web Text Summarization von Yadav, Madhuri, Katarya, Rahul

“… Extracting and concatenating distilled content from a corpus into a summary is a technique known as extractive summarization. In recent days, extractive …”
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Text to Image Synthesis using Stacked Conditional Variational Autoencoders and Conditional Generative Adversarial Networks von Tibebu, Haileleol, Malik, Aadil, De Silva, Varuna

“… Most existing studies rely either on Generative Adversarial Networks (GANs) or Variational Auto Encoders (VAEs …”
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Generative Neural Networks for Anomaly Detection in Crowded Scenes von Wang, Tian, Qiao, Meina, Lin, Zhiwei, Li, Ce, Snoussi, Hichem, Liu, Zhe, Choi, Chang

“… The S 2 -VAE consists of two proposed neural networks: a Stacked Fully Connected Variational AutoEncoder (S F -VAE …”
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A comprehensive review on encoder–decoder architectures in ECG signal compression and denoising: opportunities, challenges, and prospects von Das, Maumita, Sahana, Bikash Chandra

“… For effective real-time ECG signal transmission via wearables or telemetry systems, the denoising autoencoder (DAE …”
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Reliable Fault Diagnosis of Bearings Using an Optimized Stacked Variational Denoising Auto-Encoder von Yan, Xiaoan, Xu, Yadong, She, Daoming, Zhang, Wan

“… Therefore, in order to improve the anti-noise performance of the VAE model and adaptively select its parameters, this paper proposes an optimized stacked variational denoising autoencoder (OSVDAE …”
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VMD-SEAE-TL-Based Data-Driven soft sensor modeling for a complex industrial batch processes von Ren, Jun-Chao, Liu, Ding, Wan, Yin

“… •A stack enhanced autoencoder algorithm based on VMD is proposed in this paper. Here, VMD is implemented by decomposing and reconstructing the original data to eliminate the noise in the data …”
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Comparative Analysis of Deep Learning Algorithm for Cancer Classification using Multi-omics Feature Selection von Azmi, Nur Sabrina, A Samah, Azurah, Sirgunan, Vivekaanan, Ali Shah, Zuraini, Abdul Majid, Hairudin, Howe, Chan Weng, Wen, Nies Hui, Azman, Nuraina Syaza

“… This study aims to investigate how deep learning algorithms, namely stacked denoising autoencoder (SDAE …”
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A deep generative model of 3D single-cell organization von Donovan-Maiye, Rory M., Brown, Jackson M., Chan, Caleb K., Ding, Liya, Yan, Calysta, Gaudreault, Nathalie, Theriot, Julie A., Maleckar, Mary M., Knijnenburg, Theo A., Johnson, Gregory R.

“… We employ stacked conditional β -variational autoencoders to first learn a latent representation of cell morphology, and then learn a latent representation of subcellular structure localization which is conditioned on the learned cell morphology …”
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