Suchergebnisse - (conditional OR conditioning) autoencoder

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

Autoren: Chen, Jacky Hull, John Poulos, Zissis et al.

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

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

A conditional masked autoencoder network based on efficient multiple-head self-attention for characterizing heterogeneous reservoirs.

Autoren: Chen, Qiyu Zhou, Ruihong Chen, Dajie et al.

Quelle: Expert Systems with Applications. Jan2026:Part A, Vol. 296, pN.PAG-N.PAG. 1p.

Schlagwörter: *PETROLEUM reservoirs, *AUTOENCODERS, *GEOLOGICAL modeling, *OPTIMIZATION algorithms, *PETROLEUM engineering, *SPATIAL analysis (Statistics)

Physical discovery in representation learning via conditioning on prior knowledge.

Autoren: Liu, Yongtao Huey, Bryan D. Ziatdinov, Maxim A. et al.

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

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

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

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

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

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

Anomaly Detection for Charging Piles Based on Conditional Variational Autoencoder.

Autoren: Wang, Chuanjun Lu, Yinyu Wang, Mingxin et al.

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

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

Lightweight Text-to-Image Generation Model Based on Contrastive Language-Image Pre-Training Embeddings and Conditional Variational Autoencoders.

Autoren: Wang, Yubo Zhang, Gaofeng

Quelle: Electronics (2079-9292); Jun2025, Vol. 14 Issue 11, p2185, 31p

Schlagwörter: AUTOENCODERS, SUPPLY & demand, GENERALIZATION

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

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

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

The Use of Conditional Variational Autoencoders in Generating Stellar Spectra.

Autoren: Gebran, Marwan Bentley, Ian

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

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

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

Autoren: Yuling Sang Abhirup Banerjee Marcel Beetz et al.

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

Schlagwörter: 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

Dateibeschreibung: electronic resource

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

Zugangs-URL: https://doaj.org/article/2a10921b18e7497d98cab78e66c9b407

Multi-modal graph convolutional network for vessel trajectory prediction based on cooperative intention enhance using conditional variational autoencoder

Autoren: Jiang, Junhao Zuo, Yi Li, Zhiyuan

Quelle: Reliability Engineering and System Safety. 267

Schlagwörter: Graph convolutional network, Trajectory prediction, Cooperative intention constructor, Intelligent situational awareness systems, Conditional variational autoencoder, Multi-modal interaction extractor

Zugangs-URL: https://research.chalmers.se/publication/549377

Individualized Estimation of Baseline Retinal Nerve Fiber Layer Thickness Using Conditional Variational Autoencoder

Autoren: Ou Tan, PhD Keke Liu, MD Aiyin Chen, MD et al.

Quelle: Ophthalmology Science, Vol 5, Iss 6, Pp 100849- (2025)

Schlagwörter: Conditional variational autoencoder, Glaucoma, Nerve fiber layer thickness, OCT, Ophthalmology, RE1-994

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2666914525001472; https://doaj.org/toc/2666-9145

Zugangs-URL: https://doaj.org/article/2533b76a4100467c80b14765360ef5b5

ERLD-HC: Entropy-Regularized Latent Diffusion for Harmony-Constrained Symbolic Music Generation.

Autoren: Li, Yang

Quelle: Entropy; Sep2025, Vol. 27 Issue 9, p901, 21p

Schlagwörter: HARMONICS (Music theory), AUTOENCODERS, SONGWRITING, MIDI (Standard), DEEP learning, PROBABILISTIC generative models, MACHINE learning, MUSICAL notation

Structural Dynamics Feature Learning Using a Supervised Variational Autoencoder.

Autoren: Bacsa, Kiran Liu, Wei Abdallah, Imad et al.

Quelle: Journal of Engineering Mechanics; Feb2025, Vol. 151 Issue 2, p1-14, 14p

Schlagwörter: STRUCTURAL health monitoring, WIND turbine blades, AUTOENCODERS, STRUCTURAL dynamics, SUPERVISED learning

Trans-cVAE-GAN: Transformer-Based cVAE-GAN for High-Fidelity EEG Signal Generation.

Autoren: Yao, Yiduo Wang, Xiao Hao, Xudong et al.

Quelle: Bioengineering (Basel). Oct2025, Vol. 12 Issue 10, p1028. 40p.

Schlagwörter: *ELECTROENCEPHALOGRAPHY, *GENERATIVE adversarial networks, *TRANSFORMER models, *BRAIN-computer interfaces, *AFFECTIVE computing, *PROBABILISTIC generative models, *EMOTION recognition

DiffusionAAE: Enhancing hyperspectral image classification with conditional diffusion model and Adversarial Autoencoder

Autoren: Zeyu Cao Jinhui Li Xiangrui Xu

Quelle: Ecological Informatics, Vol 87, Iss , Pp 103118- (2025)

Schlagwörter: Land cover classification, Generative model, Feature extraction, Hyperspectral image classification, Conditional diffusion model, Adversarial autoencoder, Information technology, T58.5-58.64, Ecology, QH540-549.5

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S157495412500127X; https://doaj.org/toc/1574-9541

Zugangs-URL: https://doaj.org/article/ad41b692c5984e31b024c955e44b6c99

PREmbed: Balancing Conditional Generative Models with Embedding Pretraining and Regularization.

Autoren: Liu, Haiyang Endo, Yuki Lee, Jinho et al.

Quelle: Electronics (2079-9292); Jan2025, Vol. 14 Issue 2, p280, 23p

Schlagwörter: AUTOENCODERS, MOTIVATION (Psychology)

SWIR based estimation of TIR emissivity of bare soil surfaces using deep conditional generative adversarial network in Landsat data.

Autoren: Ataei, Shima Momeni, Mehdi Monadjemi, Amirhassan

Quelle: Plant & Soil; Apr2025, Vol. 509 Issue 1, p377-397, 21p

Schlagwörter: GENERATIVE adversarial networks, STANDARD deviations, LANDSAT satellites, REMOTE sensing, AUTOENCODERS

IgCONDA-PET: Weakly-supervised PET anomaly detection using implicitly-guided attention-conditional counterfactual diffusion modeling — a multi-center, multi-cancer, and multi-tracer study.

Autoren: Ahamed, Shadab Rahmim, Arman

Quelle: Computerized Medical Imaging & Graphics. Sep2025, Vol. 124, pN.PAG-N.PAG. 1p.

Schlagwörter: *GENERATIVE adversarial networks, *ANOMALY detection (Computer security), *POSITRON emission tomography, *AUTOENCODERS, *COUNTERFACTUALS (Logic)

A Moving Object Detection Method Based on Conditional Information and Feature Deepening.

Autoren: Zhang, Hongrui Yang, Luxia Abdellah, Islam

Quelle: Journal of Electrical & Computer Engineering; 3/31/2025, Vol. 2025, p1-12, 12p

Schlagwörter: GENERATIVE adversarial networks, AUTOENCODERS

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

Autoren: Sang, Yuling Banerjee, Abhirup Beetz, Marcel et al.

Quelle: Frontiers in Digital Health; 2025, p1-13, 13p

Schlagwörter: HEART anatomy, RISK assessment, PREDICTION models, RESEARCH funding, RECEIVER operating characteristic curves, CARDIOVASCULAR diseases risk factors, MAGNETIC resonance imaging, RELATIVE medical risk, ELECTROCARDIOGRAPHY, KAPLAN-Meier estimator, DEEP learning, SURVIVAL analysis (Biometry), ELECTRODES, PROPORTIONAL hazards models

Geografische Kategorien: UNITED Kingdom