Výsledky vyhľadávania - Gaussian mixture prior variational autoencoder

Augmenting deviation of faults from the normal using fault assistant Gaussian mixture prior variational autoencoder Autor Lee, Yi Shan, Chen, Junghui

“…•Abnormal data are used to augment the deviation of the fault from the normal.•Non-negative information sharing and transferring improve model…”
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Prior Distribution Guided Gaussian Mixture Variational Autoencoder (PDGM-VAE) for Image Generation Autor Song, Jingqi, He, Zhiqiang, Ning, Yipeng, Xi, Xiaoming, Guo, Jie, Chen, Guanzhong, Nie, Xiushan, Qiao, Lishan, Yin, Yilong

“… capture the true distribution. To address the aforementioned issues, we propose a Prior Distribution Guided Gaussian Mixture Variational Autoencoder(PDGM-VAE…”
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Enhancing monitoring performance of data sparse nonlinear processes through information sharing among different grades using Gaussian mixture prior variational autoencoders Autor Lee, Yi Shan, Chen, Junghui

“…In the chemical industry, manufacturers produce multiple products with different grades constantly by changing the operating conditions and raw materials in…”
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Variational Autoencoder with Optimizing Gaussian Mixture Model Priors Autor Guo, Chunsheng, Zhou, Jialuo, Chen, Huahua, Ying, Na, Zhang, Jianwu, Zhou, Di

“… This paper proposes a variational autoencoder with optimizing Gaussian mixture model priors…”
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Deep Multiple-sphere Support Vector Data Description Based on Variational Autoencoder with Mixture-of-Gaussians Prior Autor Wu, Huinan, Xing, Hongjie, Li, Gang

“… on variational autoencoder with mixture-of-gaussians prior（DMSVDD-VAE-MoG） is proposed.First, the network parameters and multiple hypersphere centers are initialized by pre…”
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Deep Clustering by Gaussian Mixture Variational Autoencoders With Graph Embedding Autor Yang, Linxiao, Cheung, Ngai-Man, Li, Jiaying, Fang, Jun

“…We propose DGG: Deep clustering via a Gaussian-mixture variational autoencoder (VAE) with Graph embedding…”
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A Semi-supervised Gaussian Mixture Variational Autoencoder method for few-shot fine-grained fault diagnosis Autor Zhao, Zhiqian, Xu, Yeyin, Zhang, Jiabin, Zhao, Runchao, Chen, Zhaobo, Jiao, Yinghou

“… Initially, Gaussian mixtures are introduced as a multimodal prior distribution for the Variational Autoencoder…”
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Interpretable embeddings from molecular simulations using Gaussian mixture variational autoencoders Autor Bozkurt Varolgüne, Yasemin, Bereau, Tristan, Rudzinski, Joseph F

“… While variational autoencoders ensure continuity of the embedding by assuming a unimodal Gaussian prior, this is at…”
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A Novel Model for Ship Trajectory Anomaly Detection Based on Gaussian Mixture Variational Autoencoder Autor Xie, Lei, Guo, Tao, Chang, Jiliang, Wan, Chengpeng, Hu, Xinyuan, Yang, Yang, Ou, Changkui

“… In view of this, an anomaly detection model for ship trajectory data based on Gaussian Mixture Variational Autoencoder (GMVAE…”
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A variational autoencoder for minimally-supervised pulse shape discrimination Autor Abdulaziz, Abdullah, Zhou, Jianxin, Fang, Ming, McLaughlin, Stephen, Di Fulvio, Angela, Altmann, Yoann

“…We propose a novel approach based on variational autoencoder with Gaussian mixture latent space (GMVAE…”
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A Guassian Mixture Variational Graph Autoencoder for Node Classification Autor Mohadeseh Ghayekhlou, Ahmad Nikabadi

“… Recently, Variational Graph AutoEncoders (VGAEs) have demonstrated good performance by learning smooth representations from unlabeled training samples…”
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Achieving deep clustering through the use of variational autoencoders and similarity-based loss Autor Ma, He

“… In this work, a novel variational autoencoder-based deep clustering algorithm is proposed. It treats the Gaussian mixture model as the prior latent space and uses…”
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基于混合高斯先验变分自编码器的深度多球支持向量数据描述 Autor 武慧囡, 邢红杰, 李刚

“…,提出了基于混合高斯先验变分自编码器的深度多球支持向量数据描述(Deep Multiple-Sphere Support Vector Data Description Based on Variational Autoencoder with Mixture-of-Gaussians Prior,DMSVDD-VAE…”
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Exploring Temporal Community Structure via Network Embedding Autor Li, Tianpeng, Wang, Wenjun, Jiao, Pengfei, Wang, Yinghui, Ding, Ruomeng, Wu, Huaming, Pan, Lin, Jin, Di

“…Temporal community detection is helpful to discover and analyze significant groups or clusters hidden in dynamic networks in the real world. A variety of…”
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Robust Robot Control Policy Learning Based on Gaussian Mixture Variational Autoencoders Autor Dong, Qingwei, Zeng, Peng, Zang, Chuanzhi, Wan, Guangxi, Dong, Xiaoting, Cui, Shijie

“… The Gaussian Mixture Variational Autoencoder (GMVAE) algorithm leverages a Gaussian Mixture Model (GMM…”
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Prognostics With Variational Autoencoder by Generative Adversarial Learning Autor Huang, Yu, Tang, Yufei, VanZwieten, James

“… autoencoder that is trained with generative adversarial networks. A long short-term memory network and a Gaussian mixture model are utilized as building blocks so that the model is capable of providing…”
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HGMVAE: hierarchical disentanglement in Gaussian mixture variational autoencoder Autor Zhou, Jiashuang, Liu, Yongqi, Du, Xiaoqin

“… We develop a generative network based on VAE incorporating a Gaussian mixture distribution as the prior…”
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Variational co-embedding learning for attributed network clustering Autor Yang, Shuiqiao, Verma, Sunny, Cai, Borui, Jiang, Jiaojiao, Yu, Kun, Chen, Fang, Yu, Shui

“… Technically, VCLANC consists of dual variational autoencoders that co-embed nodes and attributes into the same latent space, along with a trainable Gaussian mixture prior that simultaneously performs…”
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Variational embedding of protein folding simulations using Gaussian mixture variational autoencoders Autor Ghorbani, Mahdi, Prasad, Samarjeet, Klauda, Jeffery B, Brooks, Bernard R

“… Dimensionality reduction methods are thus required to extract useful and relevant information. Here, we devise a machine learning method, Gaussian mixture variational autoencoder (GMVAE…”
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Open-set recognition of breast cancer treatments Autor Cao, Alexander, Klabjan, Diego, Luo, Yuan

“… Accordingly, we reframe the problem methodology and apply a recent Gaussian mixture variational autoencoder model, which achieves state-of-the-art results for image datasets, to breast cancer patient data…”
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