Search Results - "Supervised variational autoencoder"

A Supervised Variational Autoencoder for Incomplete Multi‐View Classification by Xu, Yi, Chen, Anchi

“…Although significant progress has been made in multi‐view classification over the past few decades, handling multi‐view data with arbitrary view missing is…”
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Adversarial Attack Type I: Cheat Classifiers by Significant Changes by Tang, Sanli, Huang, Xiaolin, Chen, Mingjian, Sun, Chengjin, Yang, Jie

“…Despite the great success of deep neural networks, the adversarial attack can cheat some well-trained classifiers by small permutations. In this paper, we…”
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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

“…Climate change and excessive exploitation of water resources exert pressure on groundwater supply and the ecosystem in drylands. Although The Gravity Recovery…”
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A supervised variational autoencoder framework for dimensionality reduction and predictive modeling in high-dimensional socioeconomic data by Xue, Pei, Li, Tianshun

“…We introduce an estimation framework utilizing a Supervised Variational Autoencoder (SVAE) to address challenges posed by high-dimensional socioeconomic data…”
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SMAS: Structural MRI-based AD Score using Bayesian supervised VAE by Nemali, A., Bernal, J., Yakupov, R., Singh, D., Dyrba, M., Incesoy, E.I., Mukherjee, S., Peters, O., Ersözlü, E., Hellmann-Regen, J., Preis, L., Priller, J., Spruth, E., Altenstein, S., Lohse, A., Schneider, A., Fliessbach, K., Kimmich, O., Wiltfang, J., Hansen, N., Schott, B., Rostamzadeh, A., Glanz, W., Butryn, M., Buerger, K., Janowitz, D., Ewers, M., Perneczky, R., Rauchmann, B., Teipel, S., Kilimann, I., Goerss, D., Laske, C., Sodenkamp, S., Spottke, A., Coenjaerts, M., Brosseron, F., Lüsebrink, F., Dechent, P., Scheffler, K., Hetzer, S., Kleineidam, L., Stark, M., Jessen, F., Duzel, E., Ziegler, G.

“…This study introduces the Structural MRI-based Alzheimer’s Disease Score (SMAS), a novel index intended to quantify Alzheimer’s Disease (AD)-related…”
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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

“…One common serious issue of training a prediction model is that the process data significantly outnumber the quality data. Such discrepancy exists because of…”
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Adversarial Training-Based Deep Layer-Wise Probabilistic Network for Enhancing Soft Sensor Modeling of Industrial Processes by Xie, Yongfang, Wang, Jie, Xie, Shiwen, Chen, Xiaofang

“…Improving the robustness of the soft sensor model of industrial processes is an important yet challenging problem for a large amount of noise interference and…”
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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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Deep Feature Learning for Multi-Omics Clustering Using Supervised Variational Autoencoders with Clinical Information by Shi, Tianyi, Ye, Xiucai, Sakurai, Tetsuya

“…Identifying cancer subtypes through multi-omics clustering holds potential to advance cancer research by uncovering subtype-specific mechanisms. Most existing…”
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Soft Sensor Method based on Quality-related Virtual Sample Generation and Sample-weighted Learning by Dong, Shuang, Jin, Huaiping, Wang, Bin, Yang, Biao, Liu, Haipeng

“…In process industry, data-driven soft sensor often faces the problem of data shortage in modeling due to factors such as high cost of label samples acquisition…”
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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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Learning Interpretable and Robust Spatiotemporal Dynamics from fMRI for Precise Identification of Neurological Disorders by Li, Youhao, Huang, Yongzhi, Gao, Qingchen, Chen, Pindong, Liu, Yong, Tu, Liyun

“…Resting-state functional magnetic resonance imaging (rs-fMRI) has significantly advanced the diagnosis of brain diseases. However, existing methods are…”
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