Search Results - "semi-supervised variational autoencoder"

Customization of latent space in semi-supervised Variational AutoEncoder by An, Seunghwan, Jeon, Jong-June

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A mechanical fault diagnosis model with semi-supervised variational autoencoder based on long short-term memory network by Qu, Yuanyuan, Li, Tao, Fu, Shichen, Wang, Zhisheng, Chen, Jian, Zhang, Yupeng

“… A mechanical fault diagnosis model with Semi-Supervised Variational Autoencoder based on Long Short-Term Memory network (LSTM-SSVAE…”
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Developing semi-supervised variational autoencoder-generative adversarial network models to enhance quality prediction performance by Ooi, Sai Kit, Tanny, Dave, Chen, Junghui, Wang, Kai

“… Such discrepancy exists because of the time lag for obtaining quality data. This paper proposes semi-supervised variational autoencoder-generative adversarial network (S2-VAE/GAN…”
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Semi-supervised Variational Autoencoder for WiFi Indoor Localization by Chidlovskii, Boris, Antsfeld, Leonid

“…We address the problem of indoor localization based on WiFi signal strengths. We develop a semi-supervised deep learning method able to train a prediction…”
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Semi-Supervised Variational Autoencoder for Cell Feature Extraction In Multiplexed Immunofluorescence Images by Sandarenu, Piumi, Chen, Julia, Slapetova, Iveta, Browne, Lois, Graham, Peter H., Swarbrick, Alexander, Millar, Ewan K.A., Song, Yang, Meijering, Erik

“…Advancements in digital imaging technologies have sparked increased interest in using multiplexed immunofluorescence (mIF) images to visualise and identify the…”
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Spatial and temporal downscaling schemes to reconstruct high-resolution GRACE data: A case study in the Tarim River Basin, Northwest China by Xue, Dongping, Gui, Dongwei, Ci, Mengtao, Liu, Qi, Wei, Guanghui, Liu, Yunfei

“… of GRACE data effectively. In this study, we employ the semi-supervised variational autoencoder (SSVAER…”
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A Semi-Supervised Variational Autoencoder for Fault Detection of Low-Severity Inter-Turn Short-Circuit in PMSMs by Zhu, Mingda, Nguyen, Du, Han, Peihua, Huynh, Khang, Zhou, Jing

“… This study proposes a semi-supervised Variational Autoencoder (VAE) with Long Short-Term Memory Networks for fault detection…”
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Semi-Supervised Variational Autoencoder for Survival Prediction by Pálsson, Sveinn, Cerri, Stefano, Dittadi, Andrea, Koen Van Leemput

“…In this paper we propose a semi-supervised variational autoencoder for classification of overall survival groups from tumor segmentation masks…”
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Semi-supervised Variational Autoencoder for Regression: Application on Soft Sensors by Zhuang, Yilin, Zhou, Zhuobin, Alakent, Burak, Mercangöz, Mehmet

“…We present the development of a semi-supervised regression method using variational autoencoders (VAE), which is customized for use in soft sensing…”
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Semi-supervised variational autoencoder for cell feature extraction in multiplexed immunofluorescence images by Sandarenu, Piumi, Chen, Julia, Slapetova, Iveta, Browne, Lois, Graham, Peter H, Swarbrick, Alexander, Millar, Ewan K A, Yang, Song, Meijering, Erik

“…Advancements in digital imaging technologies have sparked increased interest in using multiplexed immunofluorescence (mIF) images to visualise and identify the…”
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A Joint Semi-Supervised Variational Autoencoder and Transfer Learning Model for Designing Molecular Transition Metal Complexes

“…Deep generative models (DGMs) have shown great promise in the generation of organic molecules and inorganic materials with chemical sensible structures and…”
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US Patent Issued to NAVER, NAVER LABS on June 28 for "Semi-supervised variational autoencoder for indoor localization" (French Inventors)

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Exploring semi-supervised variational autoencoders for biomedical relation extraction by Zhang, Yijia, Lu, Zhiyong

“…•A semi-supervised method is proposed based on variational autoencoders (VAE) for biomedical relation extraction.•Cutting-edge neural networks are used to…”
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Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders by Whiteway, Matthew R., Biderman, Dan, Friedman, Yoni, Dipoppa, Mario, Buchanan, E. Kelly, Wu, Anqi, Zhou, John, Bonacchi, Niccolò, Miska, Nathaniel J., Noel, Jean-Paul, Rodriguez, Erica, Schartner, Michael, Socha, Karolina, Urai, Anne E., Salzman, C. Daniel, Cunningham, John P., Paninski, Liam

“…Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral…”
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Semi-Supervised Variational Autoencoders for Out-of-Distribution Generation by Lavda, Frantzeska, Kalousis, Alexandros

“…Humans are able to quickly adapt to new situations, learn effectively with limited data, and create unique combinations of basic concepts. In contrast,…”
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Semi-supervised Variational Autoencoders for Regression: Application to Soft Sensors by Zhuang, Yilin, Zhou, Zhuobin, Alakent, Burak, Mercangoz, Mehmet

“…We present the development of a semi-supervised regression method using variational autoencoders (VAE) for soft sensing of process quality variables. Recently,…”
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Supervised and semi-supervised probabilistic learning with deep neural networks for concurrent process-quality monitoring by Wang, Kai, Yuan, Xiaofeng, Chen, Junghui, Wang, Yalin

“…Concurrent process-quality monitoring helps discover quality-relevant process anomalies and quality-irrelevant process anomalies. It especially works well in…”
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A semi-supervised temporal modeling strategy integrating VAE and Wasserstein GAN under sparse sampling constraints by Hu, Yujie, Xie, Changrui, Chen, Xi

“…Time series network models are widely applied in process industries for soft sensing, fault monitoring, and real-time optimization, serving as a powerful tool…”
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Zero-shot learning for action recognition using synthesized features by Mishra, Ashish, Pandey, Anubha, Murthy, Hema A.

“…). A consequence of the proposed approach is a transductive setting using a semi-supervised variational autoencoder, where the unlabelled data from unseen classes are used to train the model…”
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An evolutionary variational autoencoder for perovskite discovery by Chenebuah, Ericsson Tetteh, Nganbe, Michel, Tchagang, Alain Beaudelaire

“…Machine learning (ML) techniques emerged as viable means for novel materials discovery and target property determination. At the vanguard of discoverable…”
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