Výsledky vyhledávání - Tabular Variational Autoencoder

CTVAE: Contrastive Tabular Variational Autoencoder for imbalance data

Autoři: Alex X. Wang Minh Quang Le Huu-Thanh Duong a další

Zdroj: Knowledge and Information Systems. 67:5335-5354

Enhancing DNS-over-HTTPS Traffic Classification in Heterogeneous Networks Through Latent Space Analysis with a Tabular-Variational Autoencoder and Self-Attention Classifier Model

Autoři: Ravi Veerabhadrappa Poornima Athikatte Sampigerayappa

Zdroj: Artificial Intelligence and Applications.

Landslide Susceptibility Assessment via Imbalanced Data Augmentation with Tabular Variational Autoencoder and Quality–Diversity Post-Selection

Autoři: Zhengyang Xu Shitai Wang Min Yin a další

Zdroj: Applied Sciences, Vol 15, Iss 22, p 11965 (2025)

Témata: landslide susceptibility, risk assessment, class imbalance, data augmentation, Tabular Variational Autoencoder, quality–diversity, 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/2076-3417/15/22/11965; https://doaj.org/toc/2076-3417

Přístupová URL adresa: https://doaj.org/article/8011e17a650b45bbbd7e5af7469f76c2

Enhancing Semi-Supervised Learning in Educational Data Mining Through Synthetic Data Generation Using Tabular Variational Autoencoder

Autoři: Georgios Kostopoulos Nikos Fazakis Sotiris Kotsiantis a další

Zdroj: Algorithms, Vol 18, Iss 10, p 663 (2025)

Témata: Educational Data Mining, fairness, prediction, semi-supervised learning, synthetic data, variational auto-encoder, Industrial engineering. Management engineering, T55.4-60.8, Electronic computers. Computer science, QA75.5-76.95

Popis souboru: electronic resource

Relation: https://www.mdpi.com/1999-4893/18/10/663; https://doaj.org/toc/1999-4893

Přístupová URL adresa: https://doaj.org/article/8551ac1d69c64fb99d52e180e6984626

Explainable hybrid tabular Variational Autoencoder and feature Tokenizer Transformer for depression prediction

Autoři: Vinh Quang Tran Haewon Byeon

Zdroj: Expert Systems with Applications. 265:126084

Prediction of ultimate bearing capacity for rubberized concrete filled steel tube columns based on Tabular Variational Autoencoder method and Stacking ensemble strategy

Autoři: Zongming Song Chao Zhang Yiyan Lu

Zdroj: Structures. 70:107667

Enhancing Semi-Supervised Learning in Educational Data Mining Through Synthetic Data Generation Using Tabular Variational Autoencoder.

Autoři: Kostopoulos, Georgios Fazakis, Nikos Kotsiantis, Sotiris a další

Zdroj: Algorithms; Oct2025, Vol. 18 Issue 10, p663, 35p

Témata: DATA mining, DATA augmentation, METADATA, SUPERVISED learning, PROBABILISTIC generative models

Landslide Susceptibility Assessment via Imbalanced Data Augmentation with Tabular Variational Autoencoder and Quality–Diversity Post-Selection.

Autoři: Xu, Zhengyang Wang, Shitai Yin, Min a další

Zdroj: Applied Sciences (2076-3417); Nov2025, Vol. 15 Issue 22, p11965, 25p

Témata: LANDSLIDES, DATA augmentation, BOOSTING algorithms, NATURAL disasters, RISK assessment

CTVAE: Contrastive Tabular Variational Autoencoder for imbalance data.

Autoři: Wang, Alex X. Le, Minh Quang Duong, Huu-Thanh a další

Zdroj: Knowledge & Information Systems; Jun2025, Vol. 67 Issue 6, p5335-5354, 20p

Témata: AUTOENCODERS, DEEP learning, MACHINE learning, ARTIFICIAL intelligence, CLASSIFICATION

Variational AutoEncoders and Differential Privacy : balancing data synthesis and privacy constraints

Variational AutoEncoders och Differential Privacy : balans mellan datasyntes och integritetsbegränsningar

Autoři: Bremond, Baptiste

Témata: TVAE, Differential privacy, Tabular data, Synthetic data, DP-SGD, differentiell integritet, tabelldata, syntetiska data, Computer and Information Sciences, Data- och informationsvetenskap

Popis souboru: application/pdf

Relation: TRITA-EECS-EX ; 2024:5

Dostupnost: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-344751

Prognosticating fabric-reinforced cementitious matrix-to-masonry bond and failure mechanisms using novel tabular variational autoencoder-augmented probabilistic model.

Autoři: Kumar, Aman Marani, Afshin Abbas, Asim a další

Zdroj: Engineering Applications of Artificial Intelligence. Jan2026:Part 3, Vol. 163, pN.PAG-N.PAG. 1p.

Témata: *MASONRY, *PREDICTION models, *SUSTAINABLE construction, *DATA augmentation, *CEMENT composites, *STOCHASTIC analysis

DPTVAE: Data-driven prior-based tabular variational autoencoder for credit data synthesizing

Autoři: Yandan Tan Hongbin Zhu Jie Wu a další

Zdroj: Expert Systems with Applications. 241:122071

Data cleaning with variational autoencoders

Autoři: Eduardo, Simão Fernandes Lopes Marques Sutton, Charles Williams, Chris

Přispěvatelé: Eduardo, Simão Fernandes Lopes Marques Sutton, Charles Williams, Chris

Témata: poor quality data, outliers, corrupting errors, data cleaning, automated data cleaning, deep learning

Přístupová URL adresa: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.884176

Innovations in early detection of chronic non-communicable diseases among adolescents through an easy-to-use AutoML paradigm

Autoři: Ranković, Nevena Rankovic, Dragica Lukic, Igor

Zdroj: Ranković, N, Rankovic, D & Lukic, I 2025, 'Innovations in early detection of chronic non-communicable diseases among adolescents through an easy-to-use AutoML paradigm', Health Care Management Science, vol. 28, pp. 434-460. https://doi.org/10.1007/s10729-025-09718-6

Témata: chronic non-communicable diseases, AutoML paradigm, tabular variational autoencoder (TVAE) synthetic generation, model-agnostic/post-hoc methods

Popis souboru: application/pdf

Relation: info:eu-repo/semantics/altIdentifier/pmid/40875073; info:eu-repo/semantics/altIdentifier/wos/001563170400001; info:eu-repo/semantics/altIdentifier/pissn/1572-9389

Dostupnost: https://research.tilburguniversity.edu/en/publications/57b93fac-e4f0-4e0d-a66c-c19fb83f672f
https://doi.org/10.1007/s10729-025-09718-6
https://repository.tilburguniversity.edu/bitstreams/94a27b89-3dbf-4ab5-b313-ca462ab893f1/download

Sparse Regularized Autoencoders-Based Radiomics Data Augmentation for Improved EGFR Mutation Prediction in NSCLC.

Autoři: Munir, Muhammad Asif Shah, Reehan Ali Waheed, Urooj a další

Zdroj: Future Internet; Nov2025, Vol. 17 Issue 11, p495, 23p

Témata: LUNG cancer, EPIDERMAL growth factor receptors, RADIOMICS, AUTOENCODERS, MACHINE learning, ONCOLOGY, DATA augmentation

Dptvae: Data-Driven Prior-Based Tabular Variational Autoencoder for Credit Data Synthesizing

Autoři: Tan, Yandan Zhu, Hongbin Wu, Jie a další

Zdroj: SSRN Electronic Journal ; ISSN 1556-5068

Dostupnost: http://dx.doi.org/10.2139/ssrn.4395560

Fraud Data Generator: Modelling Sequence Data with Privacy in the Financial Fraud Domain

Gerador de Dados de Fraude: Modelação de Dados Sequenciais com Privacidade no Domínio da Fraude Financeira

Autoři: Cardoso, João Francisco Albuquerque de Campos

Témata: Machine Learning, Generative Modelling, Variational Autoencoder, Dados Tabulares, Dados Sequenciais, Tabular Data, Sequence Data

Dostupnost: https://hdl.handle.net/10316/102124

Aumento Sintético de Dados para Datasets Biológicos

Synthetic Data Augmentation for Biological Datasets

Autoři: Silva, Beatriz Rodrigues Moreira da

Témata: Machine Learning, Biologia, Generative Adversarial Networks, Variational Autoencoders, Dados Tabulares, Biology, Tabular Data

Dostupnost: https://hdl.handle.net/10316/102995

Data Augmentation with Cross-Modal Variational Autoencoders (DACMVA) for Cancer Survival Prediction.

Autoři: Rajaram, Sara Mitchell, Cassie S.

Zdroj: Information. Jan2024, Vol. 15 Issue 1, p7. 14p.

Témata: Data augmentation, Generative adversarial networks, Wilcoxon signed-rank test, Deep learning, Forecasting, Multiple imputation (Statistics)

Machine Learning Classifiers and Data Synthesis Techniques to Tackle with Highly Imbalanced COVID-19 Data.

Autoři: Naghipour, Avaz Soflaei, Mohammad Reza Abbaszadeh Bavil ghader-zefrehei, mostafa

Zdroj: Computer & Knowledge Engineering; Dec2024, Vol. 7 Issue 2, p51-64, 14p

Témata: GENERATIVE adversarial networks, ARTIFICIAL intelligence, MACHINE learning, COVID-19 pandemic, AUTOENCODERS