Search Results - Bayesian Supervised Variational Autoencoder

Learning from small medical data—robust semi-supervised cancer prognosis classifier with Bayesian variational autoencoder by Hsu, Te-Cheng, Lin, Che

“… Results We propose a robust Semi-supervised Cancer prognosis classifier with bAyesian variational autoeNcoder (SCAN…”
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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.

“…)-related morphometric patterns using a deep learning Bayesian-supervised Variational Autoencoder (Bayesian-SVAE…”
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R/C buildings’ seismic damage prediction based on semi-supervised automatic differentiation variational inference deep autoencoder by Demertzis, K, Kostinakis, K, Morfidis, K, Iliadis, L

“…Structural damage from earthquakes has been assessed using a variety of methodologies, both statistical and, more recently, utilizing Machine Learning (ML)…”
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Structural MRI‐Based AD Score using Bayesian VAEs by Nemali, Aditya, Moyano, Jose Bernal, Yakupov, Renat, Schütze, Hartmut, Spottke, Annika, Ramirez, Alfredo, Schneider, Anja, Metzger, Coraline D., Laske, Christoph, Bittner, Daniel, Heneka, Michael T., Peters, Oliver, Speck, Oliver, Glanz, Wenzel, Wagner, Michael, Jessen, Frank, Düzel, Emrah, Ziegler, Gabriel

“…). To address these issues, we here propose a Structural MRI‐Based AD Score (SMAS) using a Bayesian supervised Variational Autoencoder (Bayesian sVAE…”
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Statistical Speech Enhancement Based on Probabilistic Integration of Variational Autoencoder and Non-Negative Matrix Factorization by Bando, Yoshiaki, Mimura, Masato, Itoyama, Katsutoshi, Yoshii, Kazuyoshi, Kawahara, Tatsuya

“…This paper presents a statistical method of single-channel speech enhancement that uses a variational autoencoder (VAE…”
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Reverse Variational Autoencoder: A probabilistic inference framework for structural health monitoring and inverse analysis in engineering by Zhao, Hao-yang, Wang, Chen, Fan, Jian-sheng

“… This study proposes a Bayesian deep learning-based framework, termed Reverse Variational Auto-Encoder (RVAE…”
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An Overview of Variational Autoencoders for Source Separation, Finance, and Bio-Signal Applications by Singh, Aman, Ogunfunmi, Tokunbo

“… Variational Autoencoders (VAEs) can be regarded as enhanced Autoencoders where a Bayesian approach is used to learn the probability distribution of the input…”
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SMAS: Structural MRI-Based AD Score using Bayesian VAE by Nemali, Aditya, Bernal, Jose, Yakupov, Renat, Singh, Devesh, Dyrba, Martin, Incesoy, Enise, Mukherjee, Sach, Peters, Oliver, Hellmann, Julian, Ersoezlue, Ersin, Preis, Lukas, Priller, Josef, Spruth, Eike, Altenstein, Slawek, Lohse, Andrea, Schneider, Anja, Fliessbach, Klaus, Kimmich, Okka, Wiltfang, Jens, Hansen, Niels, Schott, Bjoernhendrik, Rostamzadeh, Ayda, Glanz, Wenzel, Butryn, Michaela, Buerger, Katharina, Janowitz, Daniel, Ewers, Michael, Perneczky, Robert, Rauchmann, Boris, Teipel, Stefan, Kilimann, Ingo, Goerss, Doreen, Laske, Christoph, Sodenkamp, Sebastian, Spottke, Annika, Coenjaerts, Marie, Brosseron, Frederic, Falk Luesebrink, Dechent, Peter, Scheffler, Klaus, Hetzer, Stefan, Kleineidam, Luca, Stark, Melina, Jessen, Frank, Duezel, Emrah, Ziegler, Gabriel

“…) related morphometric patterns using a deep learning Bayesian supervised Variational Autoencoder (Bayesian sVAE…”
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Combining deep generative and discriminative models for Bayesian semi-supervised learning by Gordon, Jonathan, Hernández-Lobato, José Miguel

“…•Modelling framwork that enables Bayesian semi-supervised learning.•Bayesian semi-supervised improves overall performance and uncertainty calibration…”
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Optimizing training trajectories in variational autoencoders via latent Bayesian optimization approach by Biswas, Arpan, Vasudevan, Rama, Ziatdinov, Maxim, Kalinin, Sergei V

“…Unsupervised and semi-supervised ML methods such as variational autoencoders (VAE) have become widely adopted across multiple areas of physics, chemistry…”
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Multimodal Weibull Variational Autoencoder for Jointly Modeling Image-Text Data by Wang, Chaojie, Chen, Bo, Xiao, Sucheng, Wang, Zhengjue, Zhang, Hao, Wang, Penghui, Han, Ning, Zhou, Mingyuan

“… To alleviate the time-consuming Gibbs sampler adopted by traditional topic models in the testing stage, we construct a Weibull-based variational inference network (encoder…”
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Deep clustering analysis via variational autoencoder with Gamma mixture latent embeddings by Guo, Jiaxun, Fan, Wentao, Amayri, Manar, Bouguila, Nizar

“…This article proposes a novel deep clustering model based on the variational autoencoder (VAE…”
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Grade: Generative graph contrastive learning for multimodal recommendation by Ping, Yu-Chao, Wang, Shu-Qin, Yang, Zi-Yi, Dong, Yong-Quan, Hu, Meng-Xiang, Zhang, Pei-Lin

“…-modal through variational graph reconstruction, effectively aligning modal features to improve user and item representations…”
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Semisupervised Generative Autoencoder for Single-Cell Data by Trong, Trung Ngo, Mehtonen, Juha, González, Gerardo, Kramer, Roger, Hautamäki, Ville, Heinäniemi, Merja

“… process, thus forming a SemI-SUpervised generative Autoencoder (SISUA) model. The generative model is based on the deep variational autoencoder (VAE…”
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Nonparametric Bayesian transfer learning for robust cardiopulmonary diseases classification in X-ray images by Haftu, Kibrom, Assabie, Yaregal

“… The deep transfer learning component extracts domain-invariant discriminating features using an Indian buffet process-driven variational autoencoder…”
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Novel Semi-Supervised Seasonal-Trend VTN for Multimode Process IoT Soft Sensing by He, Yan-Lin, Zhou, Yang-Xiao-Yu, Xu, Yuan, Zhu, Qun-Xiong, Li, Xingyuan

“… Built on a variational Bayesian framework, ST-VTN models the regression…”
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M2D-VAE: Self-Supervised Probabilistic Temporal-Spatial Latent Representation Learning for Unsupervised Industrial Operational Applications Under Missing Value Interference by Dai, Qingyang, Zhao, Chunhui, Huang, Biao

“… First, a novel deep dynamic probabilistic latent variable model, named Markov dynamic variational autoencoder (MD-VAE…”
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Context-Aware Learning for Generative Models by Perdikis, Serafeim, Leeb, Robert, Chavarriaga, Ricardo, Millan, Jose del R.

“… Using finite mixture models (FMMs) as the prototypical Bayesian network, we show that maximum-likelihood estimation (MLE…”
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Semi-Supervised Multichannel Speech Enhancement With a Deep Speech Prior by Sekiguchi, Kouhei, Bando, Yoshiaki, Nugraha, Aditya Arie, Yoshii, Kazuyoshi, Kawahara, Tatsuya

“…This paper describes a semi-supervised multichannel speech enhancement method that uses clean speech data for prior training…”
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Unsupervised Anomaly Detection in IoT Systems for Smart Cities by Guo, Yifan, Ji, Tianxi, Wang, Qianlong, Yu, Lixing, Min, Geyong, Li, Pan

“… in manufacturing, intrusion detection in system security, fault detection in system monitoring. Many existing schemes are problem specific and supervised approaches, which require domain knowledge and tremendous data labeling efforts…”
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