Reconstruction of Cultural Heritage in Virtual Space Following Disasters by Guanlin Chen, Yiyang Tong, Yuwei Wu, Yongjin Wu, Zesheng Liu, Jianwen Huang

“…To enhance immersion, Vector Quantized Variational Autoencoder–based audio reconstruction was used to embed personalized ambient soundscapes into the virtual space. …”
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Accelerating Energy Forecasting with Data Dimensionality Reduction in a Residential Environment by Rafael Gonçalves, Diogo Magalhães, Rafael Teixeira, Mário Antunes, Diogo Gomes, Rui L. Aguiar

“…Then, in order to mitigate the long training time, we apply principal component analysis (PCA) and a variational autoencoder (VAE) for feature reduction. …”
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Text-Conditioned Diffusion-Based Synthetic Data Generation for Turbine Engine Sensor Analysis and RUL Estimation by Luis Pablo Mora-de-León, David Solís-Martín, Juan Galán-Páez, Joaquín Borrego-Díaz

“…These components, combined with engine identifiers and cycle information, form compact 19 × 19 × 3 pixel images, later scaled to 512 × 512 × 3 pixels. A variational autoencoder (VAE)-based diffusion model, fine-tuned on these images, leverages text prompts describing engine characteristics to generate high-quality synthetic samples. …”
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Analysis of VAE-LSTM Performance in Detecting Anomalies in Average Daily Temperature Data in Jakarta 2000-2023 by INDRI RAMDANI, YENNI ANGRAINI, INDAHWATI INDAHWATI

“…The method combines generative methods and can extract complex features, such as variational autoencoder (VAE), along with the temporal coding capabilities of long-short-term memory (LSTM), a type of Recurrent Neural Network (RNN). …”
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GramSeq-DTA: A Grammar-Based Drug–Target Affinity Prediction Approach Fusing Gene Expression Information by Kusal Debnath, Pratip Rana, Preetam Ghosh

“…We applied a Grammar Variational Autoencoder (GVAE) for drug feature extraction and utilized two different approaches for protein feature extraction as follows: a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN). …”
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Generative Data Modelling for Diverse Populations in Africa: Insights from South Africa by Sally Sonia Simmons, John Elvis Hagan, Thomas Schack

“…The study, therefore, examined the efficacy of Conditional Tabular GAN (CTGAN), CopulaGAN, and Tabula Variational Autoencoder (TVAE) for generating synthetic but realistic demographic and health data. …”
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Enhanced Conditional GAN for High-Quality Synthetic Tabular Data Generation in Mobile-Based Cardiovascular Healthcare by Malak Alqulaity, Po Yang

“…Comprehensive experiments were conducted to compare the proposed architecture with two established models: Conditional Tabular GAN (CTGAN) and Tabular Variational AutoEncoder (TVAE). The evaluation utilized metrics such as the Kolmogorov–Smirnov (KS) test for continuous variables, the Jaccard coefficient for categorical variables, and pairwise correlation analyses. …”
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Assessing the generalization capabilities of TCR binding predictors via peptide distance analysis. by Leonardo V Castorina, Filippo Grazioli, Pierre Machart, Anja Mösch, Federico Errica

“…In our analysis we use several state-of-the-art models for TCR-peptide binding prediction: Attentive Variational Information Bottleneck (AVIB), NetTCR-2.0 and -2.2, and ERGO II (pre-trained autoencoder) and ERGO II (LSTM). …”
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VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security by Yonas Teweldemedhin Gebrezgiher, Sekione Reward Jeremiah, Stefanos Gritzalis, Jong Hyuk Park

“…This paper proposes a real-time anomaly detection framework that integrates the reconstruction capabilities of Variational Autoencoders (VAEs) with the feature extraction power of Convolutional Neural Networks (CNNs). …”
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Study on the quantitative analysis of Tilianin based on Raman spectroscopy combined with deep learning. by Wen Jiang, Wei Liu, Xiaotong Xin, Wei Zhang, Junhui Chen, Jieyu Liu, Yanqi Ma, Cheng Chen, Xiaomei Pan

“…In this paper, five sets of comparison models are set up, including two machine learning models (Random Forest, K-Nearest Neighbors, Artificial Neural Network) and two deep learning models (Convolutional Neural Network and Variational Autoencoder), and the results show that the model in this paper fits the best, obtaining an R2 of 0.9144, as well as a small error.…”
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Deep Learning-Based Denoising for Optical Coherence Tomography: Evaluating Self-Supervised and Generative Models Across Retinal Datasets by Diogen BABUC, Alesia LOBONŢ, Alexandru FARCAŞ, Todor IVAŞCU, Sebastian-Aurelian ŞTEFĂNIGĂ

“…The DnCNN and U-Net Autoencoder exhibited moderate performance, with slightly higher loss values, likely due to their sensitivity to fine structural variations. …”
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Dissection of tumoral niches using spatial transcriptomics and deep learning by Karla Paniagua, Yu-Fang Jin, Yidong Chen, Shou-Jiang Gao, Yufei Huang, Mario Flores

“…Summary: This study introduces TG-ME, an innovative computational framework that integrates transformer with graph variational autoencoder (GraphVAE) models for dissection of tumoral niches using spatial transcriptomics data and morphological images. …”
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Deep generative model for the inverse design of Van der Waals heterostructures by Shikun Gao, Qinyuan Huang, Chen Huang, Cheng Li, Kaihao Liu, Baisheng Sa, Yadong Yu, Dezhen Xue, Zhe Liu, Mengyan Dai

“…Abstract This study proposes ConditionCDVAE+, a crystal diffusion variational autoencoder (CDVAE) based deep generative model for inverse design of van der Waals (vdW) heterostructures. …”
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Uncertainty‐aware nuclear power turbine vibration fault diagnosis method integrating machine learning and heuristic algorithm by Ruirui Zhong, Yixiong Feng, Puyan Li, Xuanyu Wu, Ao Guo, Ansi Zhang, Chuanjiang Li

“…Then, a feature extraction method integrating variational mode decomposition (VMD), L‐cliffs‐based effective mode selection, and sample entropy is devised to extract the latent features from the complex high‐dimensional feature space. …”
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Unsupervised detection of high-frequency oscillations in intracranial electroencephalogram: promoting a valuable automated diagnostic tool for epilepsy by Wenjing Chen, Tongzhou Kang, Md Belal Bin Heyat, Jamal E. Fatima, Yuanning Xu, Dakun Lai

“…ObjectiveThis study aims to develop an unsupervised automated method for detecting high-frequency oscillations (HFOs) in intracranial electroencephalogram (iEEG) signals, addressing the limitations of manual detection processes.MethodThe proposed method utilizes an unsupervised convolutional variational autoencoder (CVAE) model in conjunction with the short-term energy method (STE) to analyze two-dimensional time-frequency representations of iEEG signals. …”
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Generation of Shape Models of Calcified TAVR populations for Solid Mechanics Simulations by means of Deep Learning by Oldenburg Jan, Borowski Finja, Supp Laura, Öner Alper, Schmitz Klaus-Peter, Stiehm Michael

“…The key innovation lies in the utilization of a conditional Convolutional Variational Autoencoder (cCVAE) to generate realistic calcification patterns, demonstrating promising preliminary results in matching actual cohort data. …”
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Triple-effect correction for Cell Painting data with contrastive and domain-adversarial learning by Chengwei Yan, Yu Zhang, Jiuxin Feng, Heyang Hua, Zhihan Ruan, Zhen Li, Siyu Li, Chaoyang Yan, Pingjing Li, Jian Liu, Shengquan Chen

“…Here, we propose cpDistiller, a triple-effect correction method specially designed for CP data, which leverages a pre-trained segmentation model coupled with a semi-supervised Gaussian mixture variational autoencoder utilizing contrastive and domain-adversarial learning. …”
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Real-Time Monitoring and Simulation of Multi-User Electricity Metering Anomaly Data Based on Distributed System by Kai Liu, Xuchao Jia, Junlong Wang, Xun Ma, Jiadong Li

“…To overcome these challenges, this study introduces a V-LSTM framework, an innovative approach that combines a variational autoencoder (VAE) with a long short-term memory (LSTM) network for anomaly detection in distributed power metering systems. …”
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Gesture recognition for hearing impaired people using an ensemble of deep learning models with improving beluga whale optimization-based hyperparameter tuning by Mohammed Assiri, Mahmoud M. Selim

“…In addition, an ensemble of classification processes, such as bidirectional gated recurrent unit (BiGRU), Variational Autoencoder (VAE), and bidirectional long short-term memory (BiLSTM) technique, is employed. …”
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Multifractal-Aware Convolutional Attention Synergistic Network for Carbon Market Price Forecasting by Liran Wei, Mingzhu Tang, Na Li, Jingwen Deng, Xinpeng Zhou, Haijun Hu

“…The dynamic error correction (DEC) module models error commonality through variational autoencoder (VAE), and uncertainty-guided dynamic weighting achieves robust error correction. …”
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