Výsledky vyhledávání - improved conditional variational autoencoder*

A Radio Map Estimation Method Based on Improved Conditional Variational Autoencoder

Autoři: Zhuang An Zheng Dou Qi Liu a další

Zdroj: 2024 International Conference on Intelligent Communication, Sensing and Electromagnetics (ICSE). :9-13

Daily Power Scenario Generation Method for Multiple Wind Farms Based on Gaussian Mixture Clustering and Improved Conditional Variational Autoencoder

Autoři: Dan LI Yunyan LIANG Shuwei MIAO a další

Zdroj: Zhongguo dianli, Vol 57, Iss 12, Pp 17-29 (2024)

Témata: QC501-721, hyperspherical distribution, TK1001-1841, Production of electric energy or power. Powerplants. Central stations, Electricity, feature extraction, wind power scenario generation, conditional variational autoencoder, gaussian mixture model, regularization technique

Přístupová URL adresa: https://doaj.org/article/4465b91aecc5458fbe897fd9f023c8fb

A Hybrid Model for QoS Prediction based on Improved Conditional Variational Autoencoder

Autoři: Mengwei Wu Qin Lu Yingxue Wang a další

Zdroj: 2022 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom). :339-346

The Recommendation Algorithm Based on Improved Conditional Variational Autoencoder and Constrained Probabilistic Matrix Factorization

Autoři: Yunfei Zhang Hongzhen Xu Xiaojun Yu

Zdroj: Applied Sciences, Vol 13, Iss 21, p 12027 (2023)

Témata: collaborative filtering, conditional variational autoencoder, constrained probabilistic matrix factorization, auxiliary information, feature matric, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Popis souboru: electronic resource

Relation: https://www.mdpi.com/2076-3417/13/21/12027; https://doaj.org/toc/2076-3417

Přístupová URL adresa: https://doaj.org/article/626d905e644d47f09bec7f9431b82667

Improving the Classification Effectiveness of Intrusion Detection by Using Improved Conditional Variational AutoEncoder and Deep Neural Network

Autoři: Yanqing Yang Kangfeng Zheng Chunhua Wu a další

Zdroj: Sensors, Vol 19, Iss 11, p 2528 (2019)

Témata: intrusion detection, variational inference, improved conditional variational autoencoder, generator network, deep neural network, Chemical technology, TP1-1185

Popis souboru: electronic resource

Relation: https://www.mdpi.com/1424-8220/19/11/2528; https://doaj.org/toc/1424-8220

Přístupová URL adresa: https://doaj.org/article/992c6ab3bdf848209c95c98257738587

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

Predicting Critical Heat Flux with Uncertainty Quantification and Domain Generalization Using Conditional Variational Autoencoders and Deep Neural Networks

Autoři: Alsafadi, Farah Furlong, Aidan Wu, Xu

Témata: Computer Science - Machine Learning, text

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

基于高斯混合聚类和改进条件变分自编码的 多风电场功率日场景生成方法. (Chinese)

Alternate Title: Daily Power Scenario Generation Method for Multiple Wind Farms Based on Gaussian Mixture Clustering and Improved Conditional Variational Autoencoder. (English)

Autoři: 李丹 梁云嫣 缪书唯 a další

Zdroj: Electric Power; Dec2024, Vol. 57 Issue 12, p17-29, 13p

Conditional Variational Autoencoder with Balanced Pre-training for Generative Adversarial Networks

Autoři: Yao, Y Wang, X Ma, Y a další

Témata: Conference Proceeding

URL: http://hdl.handle.net/10453/166744
Proceedings - 2022 IEEE 9th International Conference on Data Science and Advanced Analytics, DSAA 2022
2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)
10.1109/DSAA54385.2022.10032367

Conditional Variational Autoencoder with Balanced Pre-training for Generative Adversarial Networks

Autoři: Yao, Yuchong Wangr, Xiaohui Ma, Yuanbang a další

Témata: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, text

URL: http://arxiv.org/abs/2201.04809

Deep convolution neural network for attention decoding in multi-channel EEG with conditional variational autoencoder for data augmentation

Autoři: Tanveer, M Asjid

Témata: Technology and Engineering, H3

URL: http://lup.lub.lu.se/student-papers/record/9136748

Design of Metamaterials for Absorbers Based on Variational Autoencoder

Autoři: Qi Li Jianwei Wang Tao Lei a další

Zdroj: IEEE Access, Vol 12, Pp 92328-92336 (2024)

Témata: conditional VAE, deep learning, inverse design, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, Absorber, 0210 nano-technology, 01 natural sciences, TK1-9971, 0104 chemical sciences

Přístupová URL adresa: https://doaj.org/article/6db83ed5fe3e41cb8ff31ff2948b0121

Design of an Improved Model for Music Sequence Generation Using Conditional Variational Autoencoder and Conditional GAN

Autoři: Pallavi Ganorkar Anagha Rathkanthiwar

Zdroj: 2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI). :1-4

STO-CVAE: state transition-oriented conditional variational autoencoder for data augmentation in disability classification

Autoři: Seong Jin Bang Min Jung Kang Min-Goo Lee a další

Zdroj: Complex & Intelligent Systems, Vol 10, Iss 3, Pp 4201-4222 (2024)

Témata: Conditional variational autoencoder, Human pose estimation, Electronic computers. Computer science, 0202 electrical engineering, electronic engineering, information engineering, QA75.5-76.95, Information technology, 02 engineering and technology, Class imbalance problem, Human action recognition, T58.5-58.64

Přístupová URL adresa: https://doaj.org/article/c72756fe6c6b4986a006e7d63158d538

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

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

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

Autoři: Yubo Wang Gaofeng Zhang

Zdroj: Electronics. 14:2185

Non-linear Multi Omics Data Integration Method Using Conditional Variational Autoencoders

Autoři: Gustinna Wadu, Dimuth Adeepa Gunarathne Chekouo, Thierry Wu, Jingjing a další

Přispěvatelé: Gustinna Wadu, Dimuth Adeepa Gunarathne Chekouo, Thierry Wu, Jingjing a další

Témata: Multi-Omics Data, Statistics, Conditional Variational Autoencoder, FOS: Mathematics, Biostatistics

Popis souboru: application/pdf

Přístupová URL adresa: https://hdl.handle.net/1880/120697
https://doi.org/10.11575/prism/48306

Conditional variational autoencoder to improve neural audio synthesis for polyphonic music sound

Autoři: Lee, Seokjin Kim, Minhan Shin, Seunghyeon a další

Témata: Computer Science - Sound, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing, text

URL: http://arxiv.org/abs/2211.08715

Fully Data-Driven Pseudohealthy Synthesis for Planning Valve-Sparing Aortic Root Reconstruction using Conditional Variational Autoencoders

Autoři: Floris Ernst Mohamad Mehdi Jannis Hagenah

Zdroj: Current Directions in Biomedical Engineering, Vol 6, Iss 3, Pp 284-287 (2020)

Témata: representation learning, 03 medical and health sciences, 0302 clinical medicine, deep learning, Medicine, conditional variational autoencoder, aortic root reconstruction

Přístupová URL adresa: https://www.degruyter.com/document/doi/10.1515/cdbme-2020-3072/pdf
https://doaj.org/article/5f30e25b44c345409adfbe20cf2216a2
https://www.degruyter.com/view/journals/cdbme/6/3/article-p284.xml