Suchergebnisse - Conditional variational autoencoder (cVAE)~

Conditional Variational Autoencoder (cVAE) for bio-plastic generation

Autoren: Choo, Hyunkyung Pakarinen, Olli Garg, Vikas Kumar

Zugangs-URL: https://cris.vtt.fi/en/publications/6296e553-af7a-49d8-a033-b2787f5771b6

Enhancing Global Surface Soil Moisture Estimation From ESA CCI and SMAP Product With a Conditional Variational Autoencoder

Autoren: Changjiang Shi Zhijie Zhang Shengqing Xiong et al.

Quelle: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 9337-9359 (2024)

Schlagwörter: Conditional variational autoencoder (CVAE), SMAP L4, QC801-809, Geophysics. Cosmic physics, 0207 environmental engineering, 02 engineering and technology, 15. Life on land, surface soil moisture (SM), 01 natural sciences, Ocean engineering, product reconstruction, 13. Climate action, ESA climate change initiative (CCI), TC1501-1800, 0105 earth and related environmental sciences

Zugangs-URL: https://doaj.org/article/172f29a7106a48fb9baccd27a3d5514e

Generative design of RHS/SHS profiles using Conditional Variational Autoencoder (CVAE)

Autoren: Müller, Andreas Kraus, Michael Anton Andras, Taras

Schlagwörter: CVAE, machine learning, local buckling, Digital design tools, high strength steel

Generating Contextual Load Profiles Using a Conditional Variational Autoencoder

Autoren: Wang, C. (author) Tindemans, Simon H. (author) Palensky, P. (author)

Quelle: 2022 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe). :1-6

Schlagwörter: FOS: Computer and information sciences, CVAE, Computer Science - Machine Learning, FOS: Electrical engineering, electronic engineering, information engineering, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, generative model, Systems and Control (eess.SY), 02 engineering and technology, synthetic data, Electrical Engineering and Systems Science - Systems and Control, load profiles, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2209.04056
http://resolver.tudelft.nl/uuid:bce7b85c-1eb4-42bf-a603-f2a6b63370e2

Generative Design of Thermoset Shape Memory Polymers Driven by Chemical Group: A Conditional Variational Autoencoder Approach

Autoren: Borun Das Andrew Peters Guoqiang Li et al.

Quelle: Journal of Polymer Science. 63:1334-1344

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

Synthetic Data Generation for Enhancing Text Classification Performance Using Conditional Variational Autoencoders

Autoren: Ömer Faruk Cebeci Mehmet Fatih Amasyali

Quelle: Orclever Proceedings of Research and Development. 5:498-514

Accelerating Bayesian Sampling for Massive Black Hole Binaries with Prior Constraints from Conditional Variational Autoencoder

Autoren: Sun, Hui Wang, He He, Jibo

Index Begriffe: Astrophysics - Instrumentation and Methods for Astrophysics, text

URL: http://arxiv.org/abs/2502.09266

Improving Local Fidelity and Interpretability of LIME by Replacing Only the Sampling Process With CVAE

Autoren: Daisuke Yasui Hiroshi Sato

Quelle: IEEE Access, Vol 13, Pp 53084-53099 (2025)

Schlagwörter: local fidelity, conditional variational autoencoder, Electrical engineering. Electronics. Nuclear engineering, LIME, interpretability, TK1-9971

Zugangs-URL: https://doaj.org/article/d01705e7b3f54a5c8f2cb42f42a57459

Development of a conditional variational autoencoder for handwritten digit recognition

Autoren: Podoliak B Filimonova T Yurchenko Yu

Quelle: Artificial Intelligence. 29:75-81

Accelerating Bayesian sampling for massive black hole binaries with prior constraints from conditional variational autoencoder

Autoren: Hui Sun He Wang Jibo He

Quelle: Physical Review D. 111

Schlagwörter: FOS: Physical sciences, Astrophysics - Instrumentation and Methods for Astrophysics, Instrumentation and Methods for Astrophysics (astro-ph.IM)

Zugangs-URL: http://arxiv.org/abs/2502.09266

The Use of Conditional Variational Autoencoders in Generating Stellar Spectra

Autoren: Marwan Gebran Ian Bentley

Quelle: Astronomy, Vol 4, Iss 3, p 13 (2025)

Schlagwörter: stellar spectroscopy, stellar parameters, deep learning, conditional variational autoencoders, Astronomy, QB1-991

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2674-0346/4/3/13; https://doaj.org/toc/2674-0346

Zugangs-URL: https://doaj.org/article/7ac78b0d03814d2e93862d635ac25b4e

Aviation Fuel Pump Fault Diagnosis Based on Conditional Variational Self-Encoder Adaptive Synthetic Less Data Enhancement

Autoren: Tiejun Liu Yaoping Zhang Xiaojing Yin et al.

Quelle: Mathematics, Vol 13, Iss 14, p 2218 (2025)

Schlagwörter: data imbalance, QA1-939, fault diagnosis, aviation fuel pumps, Mathematics, conditional variational autoencoder adaptive synthetic

Zugangs-URL: https://doaj.org/article/6506c51a390e4b8b9c3f16ce838110f0

A CVAE-based generative model for generalized B1 inhomogeneity corrected chemical exchange saturation transfer MRI at 5 T

Autoren: Ruifen Zhang Qiyang Zhang Yin Wu

Quelle: NeuroImage, Vol 312, Iss, Pp 121202-(2025)

Schlagwörter: Conditional variational autoencoder (CVAE), Adult, Deep Learning, Chemical exchange saturation transfer (CEST), Image Processing, Computer-Assisted, B1 inhomogeneity, Humans, Brain, Neurosciences. Biological psychiatry. Neuropsychiatry, Magnetic Resonance Imaging, RC321-571

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/40268259
https://doaj.org/article/5020824dce994853bd9304a5c9daf2ba

Generating multiperspective process traces using conditional variational autoencoders

Autoren: Riccardo Graziosi Massimiliano Ronzani Andrei Buliga et al.

Quelle: Process Science. 2

Schlagwörter: Conditional models, Generative AI, Process mining, Deep learning

Dateibeschreibung: application/pdf

ICVAE: Interpretable Conditional Variational Autoencoder for De Novo Molecular Design.

Autoren: Fan, Xiaqiong Fang, Senlin Li, Zhengyan et al.

Quelle: International Journal of Molecular Sciences. May2025, Vol. 26 Issue 9, p3980. 14p.

Schlagwörter: *DRUG discovery, *AUTOENCODERS, *OPTIMIZATION algorithms, *LINEAR operators, *VALUATION of real property

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)

TheUse of Conditional Variational Autoencoders in Generating Stellar Spectra

Autoren: Gebran, Marwan Bentley, Ian

Schlagwörter: Computational Physics, Instrumentation and Methods for Astrophysics, FOS: Physical sciences, Solar and Stellar Astrophysics, Space Physics, Computational Physics (physics.comp-ph), Instrumentation and Methods for Astrophysics (astro-ph.IM), Solar and Stellar Astrophysics (astro-ph.SR), Space Physics (physics.space-ph)

Zugangs-URL: http://arxiv.org/abs/2508.17059

SpateCV: cross-modality alignment regularization of cell types improves spatial gene imputation for spatial transcriptomics

Autoren: Jiaqi Yuan Junhua Yu Qianbei Yi et al.

Quelle: Journal of Translational Medicine, Vol 23, Iss 1, Pp 1-21 (2025)

Schlagwörter: Spatial transcriptomics, Single-cell RNA sequencing, Gene imputation, Conditional variational autoencoder (CVAE), Attention mechanism, Medicine

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/1479-5876

Zugangs-URL: https://doaj.org/article/a2b6ea14dfa84acc85be4d66915588f3

Predicting critical heat flux with uncertainty quantification and domain generalization using conditional variational autoencoders and deep neural networks

Autoren: Farah Alsafadi Aidan Furlong Xu Wu

Quelle: Annals of Nuclear Energy. 220:111502

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2409.05790