Výsledky vyhledávání - Conditional variational autoencoder (cVAE)

Conditional Variational Autoencoder (cVAE) for bio-plastic generation

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

Přístupová URL adresa: 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

Autoři: Changjiang Shi Zhijie Zhang Shengqing Xiong a další

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

Témata: 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

Přístupová URL adresa: https://doaj.org/article/172f29a7106a48fb9baccd27a3d5514e

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

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

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

Generating Contextual Load Profiles Using a Conditional Variational Autoencoder

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

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

Témata: 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)

Přístupová URL adresa: 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

Autoři: Borun Das Andrew Peters Guoqiang Li a další

Zdroj: 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

Autoři: Jiang, Junhao Zuo, Yi Li, Zhiyuan

Zdroj: Reliability Engineering and System Safety. 267

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

Přístupová URL adresa: https://research.chalmers.se/publication/549377

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

Autoři: Ömer Faruk Cebeci Mehmet Fatih Amasyali

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

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

Autoři: Sun, Hui Wang, He He, Jibo

Témata: 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

Autoři: Daisuke Yasui Hiroshi Sato

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

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

Přístupová URL adresa: https://doaj.org/article/d01705e7b3f54a5c8f2cb42f42a57459

Development of a conditional variational autoencoder for handwritten digit recognition

Autoři: Podoliak B Filimonova T Yurchenko Yu

Zdroj: Artificial Intelligence. 29:75-81

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

Autoři: Hui Sun He Wang Jibo He

Zdroj: Physical Review D. 111

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

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

The Use of Conditional Variational Autoencoders in Generating Stellar Spectra

Autoři: Marwan Gebran Ian Bentley

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

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

Popis souboru: electronic resource

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

Přístupová URL adresa: https://doaj.org/article/7ac78b0d03814d2e93862d635ac25b4e

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

Autoři: Tiejun Liu Yaoping Zhang Xiaojing Yin a další

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

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

Přístupová URL adresa: https://doaj.org/article/6506c51a390e4b8b9c3f16ce838110f0

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

Autoři: Ruifen Zhang Qiyang Zhang Yin Wu

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

Témata: 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

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

Generating multiperspective process traces using conditional variational autoencoders

Autoři: Riccardo Graziosi Massimiliano Ronzani Andrei Buliga a další

Zdroj: Process Science. 2

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

Popis souboru: application/pdf

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

Autoři: Fan, Xiaqiong Fang, Senlin Li, Zhengyan a další

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

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

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)

TheUse of Conditional Variational Autoencoders in Generating Stellar Spectra

Autoři: Gebran, Marwan Bentley, Ian

Témata: 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)

Přístupová URL adresa: http://arxiv.org/abs/2508.17059

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

Autoři: Jiaqi Yuan Junhua Yu Qianbei Yi a další

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

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

Popis souboru: electronic resource

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

Přístupová URL adresa: https://doaj.org/article/a2b6ea14dfa84acc85be4d66915588f3

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

Autoři: Farah Alsafadi Aidan Furlong Xu Wu

Zdroj: Annals of Nuclear Energy. 220:111502

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

Přístupová URL adresa: http://arxiv.org/abs/2409.05790