Výsledky vyhledávání - (( conditional variational autoencoder（cae） ) OR ( conditioning variational autoencoder（cvae） ))

Heartbeat detection and personal authentication using a 60 GHz Doppler sensor.

Autoři: Asano, Takuma Izumi, Shintaro Kawaguchi, Hiroshi

Zdroj: Frontiers in Digital Health; 2025, p1-12, 12p

Témata: PATIENT monitoring equipment, BIOLOGICAL models, RESEARCH funding, SOUND spectrography, BIOMETRY, HEART beat, ELECTROCARDIOGRAPHY, SECURITY systems, TECHNOLOGY, PATIENT monitoring, MICROWAVES, WAVE analysis, ALGORITHMS

Survey on sampling conditioned brain images and imaging measures with generative models.

Autoři: Cheong, Sehyoung Lee, Hoseok Kim, Won Hwa

Zdroj: Biomedical Engineering Letters; Sep2025, Vol. 15 Issue 5, p831-843, 13p

MCVAE: A New Multi-Conditional Approach for Scalable Emergency Ad Hoc Network Design and Node Localization Using Generative Variational Autoencoder.

Autoři: Jabbar, M. A. A. Abbas, A. R. Ghani, R. F.

Zdroj: International Journal of Engineering Transactions C: Aspects; Jun2026, Vol. 39 Issue 6, p1390-1401, 12p

Témata: AD hoc computer networks, EMERGENCY management, REAL-time computing, AUTOENCODERS, PROBABILISTIC generative models, WIRELESS communications, SCALABILITY

Deep conditional generative model for personalization of 12-lead electrocardiograms and cardiovascular risk prediction

Autoři: Yuling Sang Abhirup Banerjee Marcel Beetz a další

Zdroj: Frontiers in Digital Health, Vol 7 (2025)

Témata: cardiac MRI, cardiovascular disease, cardiovascular risk prediction, ECG electrodes, ECG generation, variational autoencoder, Medicine, Public aspects of medicine, RA1-1270, Electronic computers. Computer science, QA75.5-76.95

Popis souboru: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/fdgth.2025.1558589/full; https://doaj.org/toc/2673-253X

Přístupová URL adresa: https://doaj.org/article/2a10921b18e7497d98cab78e66c9b407

Synthetic data generation for the optimization of strains in metabolic engineering using latent space representations derived from a Conditional Variational Autoencoder

Autoři: Alwani, Neil (author)

Témata: Metabolic Flux Analysis, Variational Autoencoder (VAE), Probabilistic analysis, Principal Component Analysis (PCA), Combinatorial Optimization, bachelor thesis

URL: http://resolver.tudelft.nl/uuid:0f0fbe65-257d-491d-9fe5-5c1b3864dfd4

Fast Prediction of Airfoil Aerodynamic Characteristics Based on a Combined Autoencoder.

Autoři: Wang, Xu Qian, Weiqi Zhao, Tun a další

Zdroj: Symmetry (20738994); Jul2024, Vol. 16 Issue 7, p791, 17p

Témata: WIND tunnel testing, LATENT variables, REYNOLDS number, AEROFOILS, NETWORK performance

Wood-species identification based on terahertz spectral data augmentation and pseudo-label guided deep clustering.

Autoři: Wang, Yuan Wang, Zhi-Gang He, Yi-Hao a další

Zdroj: Wood Material Science & Engineering; Oct2024, Vol. 19 Issue 5, p1004-1014, 11p

Témata: WOOD, DATA augmentation, MACHINE learning, PROBLEM solving, TERAHERTZ spectroscopy, GRISELINIA littoralis

Physical discovery in representation learning via conditioning on prior knowledge.

Autoři: Liu, Yongtao Huey, Bryan D. Ziatdinov, Maxim A. a další

Zdroj: Journal of Applied Physics; 8/14/2024, Vol. 136 Issue 6, p1-13, 13p

Témata: PIEZORESPONSE force microscopy, SPECTRAL imaging, LATENT variables, EXTRAPOLATION, PRIOR learning

Artificial intelligence in geoenergy: bridging petroleum engineering and future-oriented applications.

Autoři: Kim, Sungil Kim, Tea-Woo Jo, Suryeom

Zdroj: Journal of Petroleum Exploration & Production Technology; Feb2025, Vol. 15 Issue 2, p1-60, 60p

Deep Learning for Time Series Anomaly Detection: A Survey.

Autoři: Zamanzadeh Darban, Zahra Webb, Geoffrey I. Pan, Shirui a další

Zdroj: ACM Computing Surveys; Jan2025, Vol. 57 Issue 1, p1-42, 42p

Témata: ARTIFICIAL neural networks, ARTIFICIAL intelligence, REINFORCEMENT learning, CONVOLUTIONAL neural networks, GRAPH neural networks, DEEP learning, SUPERVISED learning

A Variational Autoencoder Approach to Conditional Generation of Possible Future Volatility Surfaces.

Autoři: Chen, Jacky Hull, John Poulos, Zissis a další

Zdroj: Journal of Financial Data Science; Summer2025, Vol. 7 Issue 3, p86-114, 29p

Témata: LONG short-term memory, PROBABILISTIC generative models, LATENT variables, RETROSPECTIVE studies, ECONOMIC forecasting

Anomaly Detection for Charging Piles Based on Conditional Variational Autoencoder.

Autoři: Wang, Chuanjun Lu, Yinyu Wang, Mingxin a další

Zdroj: International Transactions on Electrical Energy Systems; 9/23/2025, Vol. 2025, p1-12, 12p

Témata: ELECTRIC vehicles, SUPERVISED learning, PROBABILISTIC generative models, OUTLIER detection, SIGNAL detection, DISTRIBUTION (Probability theory), INFRASTRUCTURE (Economics)

The Use of Conditional Variational Autoencoders in Generating Stellar Spectra.

Autoři: Gebran, Marwan Bentley, Ian

Zdroj: Astronomy (2674-0346); Sep2025, Vol. 4 Issue 3, p13, 14p

Témata: STELLAR spectra, RADIATIVE transfer, SIMULATION methods & models, SPECTRUM analysis, ASTROPHYSICS, PROBABILISTIC generative models, MACHINE learning, QUANTITATIVE research

Variational Autoencoder-Regulated Data Augmentation for Precise Estimation of Organic Pollutants in Water with Deeplearning Neural Network.

Autoři: Ashiquzzaman, Akm Kim, Jinsul Kim, Chihoon

Zdroj: Applied Artificial Intelligence; Dec2025, Vol. 39 Issue 1, p1-12, 12p

A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment.

Autoři: Elahi, Mahboob Afolaranmi, Samuel Olaiya Martinez Lastra, Jose Luis a další

Zdroj: Discover Artificial Intelligence; 12/7/2023, Vol. 3 Issue 1, p1-78, 78p

Témata: GENERATIVE adversarial networks, ARTIFICIAL intelligence, CONVOLUTIONAL neural networks, AGILE software development, DIGITAL transformation, INDUSTRIAL equipment, RADIOSTEREOMETRY

Map Completion Using Generative Models on a Custom Dataset.

Autoři: Güzel, Selma Uslu, Erkan

Zdroj: Signal, Image & Video Processing; Nov2025, Vol. 19 Issue 11, p1-8, 8p

Integrating cross-sample and cross-modal data for spatial transcriptomics and metabolomics with SpatialMETA.

Autoři: Tian, Ruonan Xue, Ziwei Chen, Yiru a další

Zdroj: Nature Communications; 10/6/2025, Vol. 16 Issue 1, p1-16, 16p

Témata: METABOLOMICS, DATA integration, CANCER treatment, DATA harmonization, LATENT variables, MULTIOMICS, RNA sequencing

CSCVAE-NID: A Conditionally Symmetric Two-Stage CVAE Framework with Cost-Sensitive Learning for Imbalanced Network Intrusion Detection.

Autoři: Wang, Zhenyu Yu, Xuejun

Zdroj: Entropy; Nov2025, Vol. 27 Issue 11, p1086, 22p

Témata: INTRUSION detection systems (Computer security), DATA augmentation, CLASSIFICATION, MACHINE learning

Conditional VAE for personalized neurofeedback in cognitive training.

Autoři: Tibermacine, Imad Eddine Russo, Samuele Scarano, Gianmarco a další

Zdroj: PLoS ONE; 10/31/2025, Vol. 20 Issue 10, p1-28, 28p

Témata: MACHINE learning, FEATURE extraction, NEUROPHYSIOLOGY, ARTIFICIAL intelligence, COGNITIVE training, ROUTINE diagnostic tests

Learning conditional variational autoencoders with missing covariates

Autoři: Ramchandran, Siddharth Tikhonov, Gleb Lönnroth, Otto a další

Přispěvatelé: Ramchandran, Siddharth Tikhonov, Gleb Lönnroth, Otto a další

Témata: Conditional VAEs, Gaussian process, Missing value imputation, Variational autoencoders, Computer and information sciences

Popis souboru: application/pdf

Relation: We would like to acknowledge the computational resources provided by Aalto Science-IT, Finland. We would also like to thank Mine Ogretir for help with the PPMI dataset as well as Manuel Haussmann for the helpful discussions and comments. This work was supported by the Academy of Finland [328401] and Bayer Oy. The PPMI dataset used in the preparation of this article was obtained from the Parkin-son's Progression Markers Initiative (PPMI) database (https://www.ppmi-info.org/access-data-specimens/download-data). For up-to-date information on the study, visit www.ppmi-info.org. PPMI - a public-private partnership - is funded by The Michael J. Fox Foundation for Parkinson's Research and funding partners, including 4D Pharma, AbbVie Inc., AcureX Therapeutics, Allergan, Amathus Therapeutics, Aligning Science Across Parkinson's (ASAP), Avid Radiopharmaceuticals, Bial Biotech, Biogen, BioLegend, Bristol Myers Squibb, CalicoLife Sciences LLC, Celgene Corporation, DaCapo Brainscience, Denali Therapeutics, The Edmond J. Safra Foundation, Eli Lilly and Company,GE Healthcare, GlaxoSmithKline, Golub Capital, Handl Therapeutics, Insitro, Janssen Pharmaceuticals, Lundbeck, Merck & Co., Inc., MesoScale Diagnostics, LLC, Neurocrine Biosciences, Pfizer Inc., Piramal Imaging, Prevail Therapeutics, F. Hoffmann-La Roche Ltd and its affiliated company Genentech Inc., Sanofi Genzyme, Servier, Takeda Pharmaceutical Company, Teva Neuroscience, Inc., UCB, Vanqua Bio, VerilyLife Sciences, Voyager Therapeutics, Inc., Yumanity Therapeutics, Inc.; Ramchandran , S , Tikhonov , G , Lönnroth , O , Tiikkainen , P & Lahdesmaki , H 2024 , ' Learning conditional variational autoencoders with missing covariates ' , Pattern Recognition , vol. 147 , 110113 . https://doi.org/10.1016/j.patcog.2023.110113; http://hdl.handle.net/10138/569684; 85177739591; 001124231300001

Dostupnost: http://hdl.handle.net/10138/569684