Suchergebnisse - Tabular Variational Autoencoder*

  1. 1

    CTVAE: Contrastive Tabular Variational Autoencoder for imbalance data: CTVAE: Contrastive Tabular Variational Autoencoder for imbalance data von Wang, Alex X., Le, Minh Quang, Duong, Huu-Thanh, Van, Bay Nguyen, Nguyen, Binh P.

    ISSN: 0219-1377, 0219-3116
    Veröffentlicht: London Springer London 01.06.2025
    Veröffentlicht in Knowledge and information systems (01.06.2025)
    “… while neglecting the relationships with the majority class. To overcome these limitations, we propose the Contrastive Tabular Variational Autoencoder (CTVAE …”
    Volltext
    Journal Article
  2. 2

    Explainable hybrid tabular Variational Autoencoder and feature Tokenizer Transformer for depression prediction von Quang Tran, Vinh, Byeon, Haewon

    ISSN: 0957-4174
    Veröffentlicht: Elsevier Ltd 15.03.2025
    Veröffentlicht in Expert systems with applications (15.03.2025)
    “… and numerical features, alongside synthetic data generated by the tabular variational autoencoder (TVAE …”
    Volltext
    Journal Article
  3. 3

    DPTVAE: Data-driven prior-based tabular variational autoencoder for credit data synthesizing von Tan, Yandan, Zhu, Hongbin, Wu, Jie, Chai, Hongfeng

    ISSN: 0957-4174, 1873-6793
    Veröffentlicht: Elsevier Ltd 01.05.2024
    Veröffentlicht in Expert systems with applications (01.05.2024)
    “… •Data-driven prior-based tabular variational autoencoder (DPTVAE) for end-to-end synthesizing credit data …”
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    Journal Article
  4. 4

    Prognosticating fabric-reinforced cementitious matrix-to-masonry bond and failure mechanisms using novel tabular variational autoencoder-augmented probabilistic model von Kumar, Aman, Marani, Afshin, Abbas, Asim, Nehdi, Moncef L.

    ISSN: 0952-1976
    Veröffentlicht: Elsevier Ltd 01.01.2026
    Veröffentlicht in Engineering applications of artificial intelligence (01.01.2026)
    “… Therefore, in this study, a tabular variational autoencoder model was implemented to synthetically augment the experimental database to capture …”
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    Journal Article
  5. 5

    Enhancing Semi-Supervised Learning in Educational Data Mining Through Synthetic Data Generation Using Tabular Variational Autoencoder von Kostopoulos, Georgios, Fazakis, Nikos, Kotsiantis, Sotiris, Dimakopoulos, Yiannis

    ISSN: 1999-4893, 1999-4893
    Veröffentlicht: Basel MDPI AG 01.10.2025
    Veröffentlicht in Algorithms (01.10.2025)
    “… This paper presents TVAE-SSL, a novel semi-supervised learning (SSL) paradigm that involves Tabular Variational Autoencoder (TVAE …”
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    Journal Article
  6. 6

    Landslide Susceptibility Assessment via Imbalanced Data Augmentation with Tabular Variational Autoencoder and Quality–Diversity Post-Selection von Xu, Zhengyang, Wang, Shitai, Yin, Min, Zhang, Xiaoyu, Lu, Zengyang, Yu, Songchao, Huang, Junjun

    ISSN: 2076-3417, 2076-3417
    Veröffentlicht: Basel MDPI AG 01.11.2025
    Veröffentlicht in Applied sciences (01.11.2025)
    “… To address this issue, this study constructed a multi-factor landslide database and employed a Tabular Variational Autoencoder (TVAE …”
    Volltext
    Journal Article
  7. 7

    Prediction of ultimate bearing capacity for rubberized concrete filled steel tube columns based on Tabular Variational Autoencoder method and Stacking ensemble strategy von Song, Zongming, Zhang, Chao, Lu, Yiyan

    ISSN: 2352-0124, 2352-0124
    Veröffentlicht: Elsevier Ltd 01.12.2024
    Veröffentlicht in Structures (Oxford) (01.12.2024)
    “… This framework integrates an advanced Tabular Variational Autoencoder (TVAE) data augmentation method and a Stacking ensemble strategy …”
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    Journal Article
  8. 8

    Enhancing DNS-over-HTTPS Traffic Classification in Heterogeneous Networks Through Latent Space Analysis with a Tabular-Variational Autoencoder and Self-Attention Classifier Model von Veerabhadrappa, Ravi, Sampigerayappa, Poornima Athikatte

    ISSN: 2811-0854, 2811-0854
    Veröffentlicht: 13.08.2025
    Veröffentlicht in Artificial Intelligence and Applications (13.08.2025)
    “… In this research study, we present a novel detection approach of these DNS patterns through low-dimensional latent representations extracted via a Tabular-Variational AutoEncoder (Tab-VAE …”
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    Journal Article
  9. 9

    Innovations in early detection of chronic non-communicable diseases among adolescents through an easy-to-Use AutoML paradigm von Rankovic, Nevena, Rankovic, Dragica, Lukic, Igor

    ISSN: 1386-9620, 1572-9389, 1572-9389
    Veröffentlicht: New York Springer US 01.09.2025
    Veröffentlicht in Health care management science (01.09.2025)
    “… To counter these issues, we utilized three AutoML frameworks - AutoGluon, H2O, and MLJAR - in conjunction with a Tabular Variational Autoencoder (TVAE …”
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    Journal Article
  10. 10

    Enhanced classification of hydraulic testing of directional control valves with synthetic data generation von Neunzig, Christian, Möllensiep, Dennis, Hartmann, Melanie, Kuhlenkötter, Bernd, Möller, Matthias, Schulz, Jürgen

    ISSN: 0944-6524, 1863-7353
    Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2023
    Veröffentlicht in Production engineering (Berlin, Germany) (01.10.2023)
    “… Production environments bring inherent system challenges that are reflected in the high-dimensional production data. The data is often nonstationary, is not …”
    Volltext
    Journal Article
  11. 11

    A synthetic data-driven machine learning approach for athlete performance attenuation prediction von Cordeiro, Mauricio C., Cathain, Ciaran O., Daly, Lorcan, Kelly, David T., Rodrigues, Thiago B.

    ISSN: 2624-9367, 2624-9367
    Veröffentlicht: Switzerland Frontiers Media S.A 27.05.2025
    Veröffentlicht in Frontiers in sports and active living (27.05.2025)
    “… This study extends previous research by evaluating Tabular Variational Autoencoders (TVAE) for generating synthetic data to predict performance attenuation in Gaelic football athletes …”
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    Journal Article
  12. 12

    Learning Performance Efficiency of College Basketball Players Using TVAE von Yahyasoltani, Nasim, Annapureddy, Priyanka, Farazi, Manzur

    ISSN: 2169-3536, 2169-3536
    Veröffentlicht: Piscataway IEEE 01.01.2023
    Veröffentlicht in IEEE access (01.01.2023)
    “… Monitoring workload is essential to evaluate possibility of fatigue or injuries and overall performance of the players. Large player's workload in any game …”
    Volltext
    Journal Article
  13. 13

    Interpretation for Variational Autoencoder Used to Generate Financial Synthetic Tabular Data von Wu, Jinhong, Plataniotis, Konstantinos, Liu, Lucy, Amjadian, Ehsan, Lawryshyn, Yuri

    ISSN: 1999-4893, 1999-4893
    Veröffentlicht: Basel MDPI AG 01.02.2023
    Veröffentlicht in Algorithms (01.02.2023)
    “… Variational Autoencoder (VAE) is one of the most popular deep-learning models for generating synthetic data …”
    Volltext
    Journal Article
  14. 14

    Evaluating Variational Autoencoder as a Private Data Release Mechanism for Tabular Data von Li, Szu-Chuang, Tai, Bo-Chen, Huang, Yennun

    ISSN: 2473-3105
    Veröffentlicht: IEEE 01.12.2019
    “… it. Regulations such as the EU's GDPR allows data exchange if data is anonymized appropriately. In this study, we use variational autoencoder as a mechanism to generate synthetic data …”
    Volltext
    Tagungsbericht
  15. 15

    Robust Variational Autoencoder for Tabular Data with Beta Divergence von Akrami, Haleh, Aydore, Sergul, Leahy, Richard M, Joshi, Anand A

    ISSN: 2331-8422
    Veröffentlicht: Ithaca Cornell University Library, arXiv.org 16.06.2020
    Veröffentlicht in arXiv.org (16.06.2020)
    “… We propose a robust variational autoencoder with \(\beta\) divergence for tabular data (RTVAE …”
    Volltext
    Paper
  16. 16

    Tabular generative modeling framework for multi-property data synthesis of pyrolyzed biochar von Yang, Yang, Li, Weishuai, Huang, Jingang, Wang, Ruilin, Han, Wei, Zhou, Rongbing, Qiu, Shanshan, Xu, Xiaobin

    ISSN: 0960-8524, 1873-2976, 1873-2976
    Veröffentlicht: England Elsevier Ltd 01.12.2025
    Veröffentlicht in Bioresource technology (01.12.2025)
    “… (CTGAN), Tabular Variational Autoencoder (TVAE), and statistical Synthpop, were developed to predict biochar properties using MultipleMICE- and MissForest …”
    Volltext
    Journal Article
  17. 17

    Deterministic Autoencoder using Wasserstein loss for tabular data generation von Wang, Alex X., Nguyen, Binh P.

    ISSN: 0893-6080, 1879-2782, 1879-2782
    Veröffentlicht: United States Elsevier Ltd 01.05.2025
    Veröffentlicht in Neural networks (01.05.2025)
    “… While Variational Autoencoders have been adapted from the computer vision domain for tabular data synthesis, their reliance on non-deterministic latent space regularization introduces limitations …”
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    Journal Article
  18. 18

    Data augmentation guided breast cancer diagnosis and prognosis using an integrated deep-generative framework based on breast tumor’s morphological information von Inan, Muhammad Sakib Khan, Hossain, Sohrab, Uddin, Mohammed Nazim

    ISSN: 2352-9148, 2352-9148
    Veröffentlicht: Elsevier Ltd 2023
    Veröffentlicht in Informatics in medicine unlocked (2023)
    “… To that end, this study investigates the potentiality of deep generative models including, the tabular variational autoencoder (TVAE …”
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    Journal Article
  19. 19

    Trigonometric-Euclidean-Smoother Interpolator (TESI) for continuous time-series and non-time-series data augmentation for deep neural network applications in agriculture von Derraz, Radhwane, Muharam, Farrah Melissa, Jaafar, Noraini Ahmad, Yap, Ng Keng

    ISSN: 0168-1699, 1872-7107
    Veröffentlicht: Elsevier B.V 01.03.2023
    Veröffentlicht in Computers and electronics in agriculture (01.03.2023)
    “… •A new method is proposed for data augmentation for deep neural network use.•The method uses a trigonometric-Euclidian space to generate the new data …”
    Volltext
    Journal Article
  20. 20

    Enhanced Conditional GAN for High-Quality Synthetic Tabular Data Generation in Mobile-Based Cardiovascular Healthcare von Alqulaity, Malak, Yang, Po

    ISSN: 1424-8220, 1424-8220
    Veröffentlicht: Switzerland MDPI AG 01.12.2024
    Veröffentlicht in Sensors (Basel, Switzerland) (01.12.2024)
    “… The generation of synthetic tabular data has emerged as a critical task in various fields, particularly in healthcare, where data privacy concerns limit the availability of real datasets for research and analysis …”
    Volltext
    Journal Article