CMImpute: cross-species and tissue imputation of species-level DNA methylation samples across mammalian species by Emily Maciejewski, Steve Horvath, Jason Ernst

“…To address this, we develop CMImpute (Cross-species Methylation Imputation), a method based on a conditional variational autoencoder, to impute DNA methylation representing species-tissue combinations. …”
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STAGE framework: A stock dynamic anomaly detection and trend prediction model based on graph attention network and sparse spatiotemporal convolutional network. by Ming Shi, Roznim Mohamad Rasli, Shir Li Wang

“…This paper proposes the STAGE framework, which integrates the Graph Attention Network (GAT), Variational Autoencoder (VAE), and Sparse Spatiotemporal Convolutional Network (STCN), to enhance the accuracy of stock prediction and the robustness of anomaly data detection. …”
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MATLAB Application for User-Friendly Design of Fully Convolutional Data Description Models for Defect Detection of Industrial Products and Its Concurrent Visualization by Fusaomi Nagata, Shingo Sakata, Keigo Watanabe, Maki K. Habib, Ahmad Shahrizan Abdul Ghani

“…Models supported by the application include the following original designs: convolutional neural network (CNN), transfer learning-based CNN, NN-based support vector machine (SVM), convolutional autoencoder (CAE), variational autoencoder (VAE), fully convolution network (FCN) (such as U-Net), and YOLO. …”
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GenConViT: Deepfake Video Detection Using Generative Convolutional Vision Transformer by Deressa Wodajo Deressa, Hannes Mareen, Peter Lambert, Solomon Atnafu, Zahid Akhtar, Glenn Van Wallendael

“…Our model combines ConvNeXt and Swin Transformer models for feature extraction, and it utilizes an Autoencoder and Variational Autoencoder to learn from latent data distributions. …”
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Deep Learning-Based Anomaly Detection in Occupational Accident Data Using Fractional Dimensions by Ömer Akgüller, Larissa M. Batrancea, Mehmet Ali Balcı, Gökhan Tuna, Anca Nichita

“…This study examines the effectiveness of Convolutional Autoencoder (CAE) and Variational Autoencoder (VAE) models in detecting anomalies within occupational accident data from the Mining of Coal and Lignite (NACE05), Manufacture of Other Transport Equipment (NACE30), and Manufacture of Basic Metals (NACE24) sectors. …”
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Atmospheric Modeling for Wildfire Prediction by Fathima Nuzla Ismail, Brendon J. Woodford, Sherlock A. Licorish

“…These include Support Vector Machine, Isolation Forest, AutoEncoder, Variational AutoEncoder, Deep Support Vector Data Description, and Adversarially Learned Anomaly Detection. …”
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Anomaly Detection of Acoustic Signals in Ultra-High Voltage Converter Valves Based on the FAVAE-AS by Shuyan Pan, Mingzhu Tang, Na Li, Jiawen Zuo, Xingpeng Zhou

“…It extracts probability features via a conditional variational autoencoder, strengthens feature representation using multi-layer convolution and residual connections, and introduces a weak correlation attention mechanism to capture global time point relationships. …”
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Hybrid-Pursuit Strategies in Multiple Pursuer-Evader Games Using Reinforcement Learning by Yacun Guan, Wang Xu, Guohua Liu

“…This paper presents a comprehensive learning strategy for the collaborative pursuit of evaders by multiple pursuers in environments with dynamic obstacles. Utilizing a variational autoencoder framework for effective obstacle detection, we integrate the multiagent twin delayed deep deterministic policy gradient algorithm for training pursuers and the proximal policy optimization algorithm for evaders, forming a complete pursuit-evasion strategy. …”
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VAE-Assisted Data Augmentation for Improved Molecular Prediction with Graph Neural Networks (GNNs) in Low-Data Regimes by Gabriela C. Theis Marchan, Pegah Naghshnejad, Andrew Okafor, Jose A. Romagnoli

“…This study presents a novel approach to enhancing molecular property prediction through variational autoencoder (VAE)-assisted data augmentation in low-data regimes. …”
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Research on geomagnetic indoor high-precision positioning algorithm based on generative model by Shuai MA, Ke PEI, Huayan QI, Hang LI, Wen CAO, Hongmei WANG, Hailiang XIONG, Shiyin LI

“…Aiming at the current bottleneck of constructing a fine geomagnetic fingerprint library that required a lot of labor costs, two generative models called the conditional variational autoencoder and the conditional confrontational generative network were proposed, which could collect a small number of data samples for a given location, and generate pseudo-label fingerprints.At the same time, in order to solve the problem of low positioning accuracy of single-point geomagnetic fingerprints, a geomagnetic sequence positioning algorithm based on attention mechanism of convolutional neural network-gated recurrent unit was designed, which could effectively use the spatial and temporal characteristics of fingerprints to achieve precise positioning.In addition, a real-time, portable mobile terminal data collection and positioning system was also designed and built.The actual test shows that the proposed model can effectively construct the available geomagnetic fingerprint database, and the average error of the proposed algorithm can reach 0.16 m.…”
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Physics-constrained superresolution diffusion for six-dimensional phase space diagnostics by Alexander Scheinker

“…Adaptive physics-constrained superresolution diffusion is developed for noninvasive virtual diagnostics of the six-dimensional (6D) phase space density of charged particle beams. An adaptive variational autoencoder embeds initial beam condition images and scalar measurements to a low-dimensional latent space from which a 32^{6} pixel 6D tensor representation of the beam's 6D phase space density is generated. …”
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cDVAE: VAE-guided diffusion for particle accelerator beam 6D phase space projection diagnostics by Alexander Scheinker

“…The diffusion process is guided by a combination of scalar parameters and images that are converted to low-dimensional latent vector representation by a variational autoencoder (VAE). We demonstrate that conditional diffusion guided by a VAE (cDVAE) can accurately reconstruct all 15 of the unique 2D projections of a charged particle beam’s 6D phase space for the HiRES compact accelerator.…”
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Image processing with Optical matrix vector multipliers implemented for encoding and decoding tasks by Minjoo Kim, Yelim Kim, Won Il Park

“…Furthermore, our approach supports noise reduction and optical image generation, enabling models such as denoising autoencoders, variational autoencoders, and generative adversarial networks. …”
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Fault Diagnosis of Magnetically Controlled On-Column Circuit Breaker Based on Small Sample Condition by He Tian, Chao Liang, Wenpeng Ma, Tianchang Zhang

“…Addressing challenges such as difficult fault signal acquisition, noise interference, and a limited number of fault samples, this paper introduces a fault diagnosis method for magnetically controlled on-column circuit breakers under conditions of small sample sizes, based on VAE-ACGAN-SDAE. Initially, a Variational Autoencoder (VAE) is employed to extract the latent distribution of genuine samples, which are then integrated with the Auxiliary Classifier Generative Adversarial Network (ACGAN) generator to learn the characteristics of real data. …”
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Semi-supervised Learning of Visual Causal Macrovariables by Aruna Jammalamadaka, Lingyi Zhang, Joseph Comer, Sasha Strelnikoff, Ryan Mustari, Tsai-Ching Lu, Rajan Bhattacharyya

“…Existing causal macrovariable discovery algorithms are limited by assumptions about known and controllable interventions. We propose a variational autoencoder-inspired architecture with regularization terms for semi-supervised causal macrovariable discovery. …”
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stDyer enables spatial domain clustering with dynamic graph embedding by Ke Xu, Yu Xu, Zirui Wang, Xin Maizie Zhou, Lu Zhang

“…We introduce stDyer, an end-to-end deep learning framework for spatial domain clustering in SRT data. stDyer combines Gaussian Mixture Variational AutoEncoder with graph attention networks to learn embeddings and perform clustering. …”
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Evaluation of Natural Image Generation and Reconstruction Capabilities Based on the β-VAE Model by Zhang Honghao

“…Natural image generation models are crucial in computer vision. However, the Variational Autoencoder (VAE) has limitations in image quality and diversity, while β-VAE achieves a balance between the decoupling of latent space and generative quality by adjusting the coefficient β. …”
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GenAI-Based Models for NGSO Satellites Interference Detection by Almoatssimbillah Saifaldawla, Flor Ortiz, Eva Lagunas, Abuzar B. M. Adam, Symeon Chatzinotas

“…In addition to the widely used autoencoder-based models (AEs), we design, develop, and train two generative AI-based models (GenAI), which are a variational autoencoder (VAE) and a transformer-based interference detector (TrID). …”
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Design of an Improved Model for Anomaly Detection in CCTV Systems Using Multimodal Fusion and Attention-Based Networks by V. Srilakshmi, Sai Babu Veesam, Mallu Shiva Rama Krishna, Ravi Kumar Munaganuri, Dulam Devee Sivaprasad

“…The utilized techniques in this paper comprise the Multimodal Deep Boltzmann Machine (MDBM), Multimodal Variational Autoencoder (MVAE) and Attention-based Fusion Networks, all of which fully utilize the learned representations. …”
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Inverse mapping of properties to composition through generative modeling for designing molten salts by Julian Barra, Rajni Chahal, Shubhojit Banerjee, Massimiliano Lupo Pasini, Stephan Irle, Stephen Lam

“…A dataset of critically evaluated molten salt densities is used to train a variational autoencoder coupled with a predictive deep neural network, which then can be used to generate new molten salt compositions with desired density values. …”
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