Výsledky vyhledávání - Tabular Variational Autoencoder

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

“… while neglecting the relationships with the majority class. To overcome these limitations, we propose the Contrastive Tabular Variational Autoencoder (CTVAE…”
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Explainable hybrid tabular Variational Autoencoder and feature Tokenizer Transformer for depression prediction Autor Quang Tran, Vinh, Byeon, Haewon

“… and numerical features, alongside synthetic data generated by the tabular variational autoencoder (TVAE…”
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DPTVAE: Data-driven prior-based tabular variational autoencoder for credit data synthesizing Autor Tan, Yandan, Zhu, Hongbin, Wu, Jie, Chai, Hongfeng

“…•Data-driven prior-based tabular variational autoencoder (DPTVAE) for end-to-end synthesizing credit data…”
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Prognosticating fabric-reinforced cementitious matrix-to-masonry bond and failure mechanisms using novel tabular variational autoencoder-augmented probabilistic model Autor Kumar, Aman, Marani, Afshin, Abbas, Asim, Nehdi, Moncef L.

“… Therefore, in this study, a tabular variational autoencoder model was implemented to synthetically augment the experimental database to capture…”
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Enhancing Semi-Supervised Learning in Educational Data Mining Through Synthetic Data Generation Using Tabular Variational Autoencoder Autor Kostopoulos, Georgios, Fazakis, Nikos, Kotsiantis, Sotiris, Dimakopoulos, Yiannis

“…This paper presents TVAE-SSL, a novel semi-supervised learning (SSL) paradigm that involves Tabular Variational Autoencoder (TVAE…”
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Landslide Susceptibility Assessment via Imbalanced Data Augmentation with Tabular Variational Autoencoder and Quality–Diversity Post-Selection Autor Xu, Zhengyang, Wang, Shitai, Yin, Min, Zhang, Xiaoyu, Lu, Zengyang, Yu, Songchao, Huang, Junjun

“… To address this issue, this study constructed a multi-factor landslide database and employed a Tabular Variational Autoencoder (TVAE…”
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Prediction of ultimate bearing capacity for rubberized concrete filled steel tube columns based on Tabular Variational Autoencoder method and Stacking ensemble strategy Autor Song, Zongming, Zhang, Chao, Lu, Yiyan

“… This framework integrates an advanced Tabular Variational Autoencoder (TVAE) data augmentation method and a Stacking ensemble strategy…”
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Enhancing DNS-over-HTTPS Traffic Classification in Heterogeneous Networks Through Latent Space Analysis with a Tabular-Variational Autoencoder and Self-Attention Classifier Model Autor Veerabhadrappa, Ravi, Sampigerayappa, Poornima Athikatte

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

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

“… Variational Autoencoder (VAE) is one of the most popular deep-learning models for generating synthetic data…”
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Enhanced classification of hydraulic testing of directional control valves with synthetic data generation Autor Neunzig, Christian, Möllensiep, Dennis, Hartmann, Melanie, Kuhlenkötter, Bernd, Möller, Matthias, Schulz, Jürgen

“…Production environments bring inherent system challenges that are reflected in the high-dimensional production data. The data is often nonstationary, is not…”
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A synthetic data-driven machine learning approach for athlete performance attenuation prediction Autor Cordeiro, Mauricio C., Cathain, Ciaran O., Daly, Lorcan, Kelly, David T., Rodrigues, Thiago B.

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

“…Monitoring workload is essential to evaluate possibility of fatigue or injuries and overall performance of the players. Large player's workload in any game…”
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Evaluating Variational Autoencoder as a Private Data Release Mechanism for Tabular Data Autor Li, Szu-Chuang, Tai, Bo-Chen, Huang, Yennun

“… 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…”
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Deterministic Autoencoder using Wasserstein loss for tabular data generation Autor Wang, Alex X., Nguyen, Binh P.

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

“…We propose a robust variational autoencoder with \(\beta\) divergence for tabular data (RTVAE…”
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Data augmentation guided breast cancer diagnosis and prognosis using an integrated deep-generative framework based on breast tumor’s morphological information Autor Inan, Muhammad Sakib Khan, Hossain, Sohrab, Uddin, Mohammed Nazim

“… To that end, this study investigates the potentiality of deep generative models including, the tabular variational autoencoder (TVAE…”
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Tabular generative modeling framework for multi-property data synthesis of pyrolyzed biochar Autor Yang, Yang, Li, Weishuai, Huang, Jingang, Wang, Ruilin, Han, Wei, Zhou, Rongbing, Qiu, Shanshan, Xu, Xiaobin

“… (CTGAN), Tabular Variational Autoencoder (TVAE), and statistical Synthpop, were developed to predict biochar properties using MultipleMICE- and MissForest…”
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Trigonometric-Euclidean-Smoother Interpolator (TESI) for continuous time-series and non-time-series data augmentation for deep neural network applications in agriculture Autor Derraz, Radhwane, Muharam, Farrah Melissa, Jaafar, Noraini Ahmad, Yap, Ng Keng

“…•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…”
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Enhanced Conditional GAN for High-Quality Synthetic Tabular Data Generation in Mobile-Based Cardiovascular Healthcare Autor Alqulaity, Malak, Yang, Po

“…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…”
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