GraphEPN: A Deep Learning Framework for B-Cell Epitope Prediction Leveraging Graph Neural Networks by Feng Wang, Xiangwei Dai, Liyan Shen, Shan Chang

“…This study presents GraphEPN, a novel B-cell epitope prediction framework combining a vector quantized variational autoencoder (VQ-VAE) with a graph transformer. …”
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Explainable Supervised Learning Models for Aviation Predictions in Australia by Aziida Nanyonga, Hassan Wasswa, Keith Joiner, Ugur Turhan, Graham Wild

“…To address the issue of class imbalance in the Australian Transport Safety Bureau (ATSB) dataset, a Variational Autoencoder (VAE) is also employed for data augmentation. …”
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UniMotion-DM: Uniform Text-Motion Generation and Editing via Diffusion Model by Song Lin, Wenjun Hou

“…UniMotion-DM integrates three core components: 1) a Contrastive Text-Motion Variational Autoencoder (CTMV), which aligns text and motion in a shared latent space using contrastive learning; 2) a controllable diffusion model tailored to the CTMV representation for generating and editing multimodal content; and 3) a Multimodal Conditional Representation and Editing (MCRE) module that leverages CLIP embeddings to enable precise and flexible control across various tasks. …”
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Decoupled Latent Diffusion Model for Enhancing Image Generation by Hyun-Tae Choi, Kensuke Nakamura, Byung-Woo Hong

“…Latent Diffusion Models have emerged as an efficient alternative to conventional diffusion approaches by compressing high-dimensional images into a lower-dimensional latent space using a Variational Autoencoder (VAE) and performing diffusion in that space. …”
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GenAI-Based Jamming and Spoofing Attacks on UAVs by Burcu Sonmez Sarikaya, Serif Bahtiyar

“…In this research, we propose a novel framework to create attacks data for UAVs by using generative artificial intelligence algorithms. We use Variational Autoencoder, Gaussian Copula, Denoising Diffusion Probabilistic Model (DDPM), and Conditional Tabular Generative Adversarial Network to create synthetic attack data. …”
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Sequential SAR-to-Optical Image Translation by Jingbo Wei, Huan Zhou, Peng Ke, Yaobin Ma, Rongxin Tang

“…To achieve this, a model based on a diffusion framework has been constructed, with twelve Transformer blocks utilized to effectively capture spatial and temporal features alternatively. A variational autoencoder is employed to encode and decode images, enabling the diffusion model to learn the distribution of features within optical image sequences. …”
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Federated Learning With Sailfish-Optimized Ensemble Models for Anomaly Detection in IoT Edge Computing Environment by Aravam Babu, A. Bagubali

“…Additionally, the framework is evaluated against leading FL-based and traditional anomaly detection models, including Local Outlier Factor (LOF), Generative Adversaria (GAN), and Variational autoencoder (VAE), demonstrating superior performance in recall and F1-score. …”
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Providing context: Extracting non-linear and dynamic temporal motifs from brain activity. by Eloy Geenjaar, Donghyun Kim, Vince Calhoun

“…In this work, we propose to use a generative non-linear deep learning model, a disentangled variational autoencoder (DSVAE), that factorizes out window-specific (context) information from timestep-specific (local) information. …”
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Autonomous Aircraft Tactical Pop-Up Attack Using Imitation and Generative Learning by Joao P. A. Dantas, Marcos R. O. A. Maximo, Takashi Yoneyama

“…To further enhance the training dataset with the aim of improving the robustness of the models, a Variational Autoencoder (VAE) was employed to generate synthetic data. …”
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A Dual-Module System for Copyright-Free Image Recommendation and Infringement Detection in Educational Materials by Yeongha Kim, Soyeon Kim, Seonghyun Min, Youngung Han, Ohyoung Lee, Wongyum Kim

“…It utilizes a Convolutional Variational Autoencoder (CVAE) model to extract significant features from copyrighted images and compares them against user-provided images. …”
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Integration of DDPM and ILUES for Simultaneous Identification of Contaminant Source Parameters and Non‐Gaussian Channelized Hydraulic Conductivity Field by Xun Zhang, Simin Jiang, Na Zheng, Xuemin Xia, Zhi Li, Ruicheng Zhang, Jiangjiang Zhang, Xinshu Wang

“…This study proposes a novel deep learning parameterization method called AEdiffusion, which combines Diffusion Denoising Probabilistic Model (DDPM) with Variational Autoencoder (VAE) for dimensionality reduction. …”
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Inverse imaging with elastic waves driven by unsupervised machine learning by Liyou Luo, Yaxi Shen, Jiawei Xi, Yabin Jin, Daniel Torrent, Jensen Li

“…Here, starting with an elastic wave imaging problem aimed at extracting material and geometry parameters, we handle this issue by adopting an unsupervised machine learning technique, variational autoencoder, to compress the observation data into a latent space whose dimensionality is leveraged to assess whether it is unique or not in the inverse process, based on readily accessible data from simulation configured to match experimental settings. …”
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Predicting the mechanical behavior in FDM printing of biopolymers through boosting artificial neural networks by M. Laurenti, I. Bavasso, E. Palazzi, J. Tirillò, F. Sarasini, F. Berto

“…The proposed approach leverages Artificial Neural Networks (ANNs), primarily utilizing a Gaussian/Gumbel-Gaussian Multi-Layer Perceptron (MLP) architecture enhanced by an encoder module derived from a Variational Autoencoder (VAE) trained on the input dataset to effectively compress and represent high-dimensional process data. …”
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DeSFAM: An Adaptive eBPF and AI-Driven Framework for Securing Cloud Containers in Real Time by Sehar Zehra, Hassan Jamil Syed, Fahad Samad, Ummay Faseeha

“…DeSFAM integrates: 1) hybrid syscall profiling through static analysis and dynamic eBPF tracing; 2) SyscallAD (System call Anomaly Detection), a low-latency anomaly detector combining Variational Autoencoder (VAE) and Isolation Forest (iForest); 3) contextual risk scoring based on MITRE ATT&CK mappings and CVE correlations; and 4) adaptive syscall enforcement using eBPF maps and LSM hooks. …”
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Evaluation Metrics and Methods for Generative Models in the Wireless PHY Layer by Michael Baur, Nurettin Turan, Simon Wallner, Wolfgang Utschick

“…Our analysis is based on real-world measurement data and includes the Gaussian mixture model, variational autoencoder, diffusion model, and generative adversarial network. …”
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Deep Learning-Driven Molecular Generation and Electrochemical Property Prediction for Optimal Electrolyte Additive Design by Dongryun Yoon, Jaekyu Lee, Sangyub Lee

“…In this study, we conducted research on Variational Autoencoder-based molecular generation and property prediction to screen for optimal molecules in the design of electrolyte additives for lithium-ion batteries. …”
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KEDM: Knowledge-Embedded Diffusion Model for Infrared Image Destriping by Lingxiao Li, Xin Wang, Dan Huang, Yunan He, Zhuqiang Zhong, Qingling Xia

“…Specifically, LDM leverages a pretrained variational autoencoder (VAE) to transform the input image into latent feature space for efficient diffusion propagation, reducing computational complexity while preserving image restoration quality. …”
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The self supervised multimodal semantic transmission mechanism for complex network environments by Jiajun Zou, Zhiping Wan, Feng Wang, Shitong Ye, Shaojiang Liu

“…The sending end employs a self-supervised conditional variational autoencoder and Transformer-DRL-based dynamic semantic compression strategy to intelligently filter and transmit the most core semantic information from video, radar, and LiDAR data. …”
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Emulation With Uncertainty Quantification of Regional Sea‐Level Change Caused by the Antarctic Ice Sheet by Myungsoo Yoo, Giri Gopalan, Matthew Hoffman, Sophie Coulson, Holly Kyeore Han, Christopher K. Wikle, Trevor Hillebrand

“…We build a physics‐based emulator using a recent sensitivity kernel approach and compare it to machine learning based emulators (neural network and conditional variational autoencoder methods). In order to quantify uncertainty, we derive well‐calibrated prediction intervals for regional sea‐level change via split‐conformal inference and linear regression, and show that Monte Carlo dropout does not yield well‐calibrated uncertainties in this instance. …”
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Interpretable Machine Learning Models and Symbolic Regressions Reveal Transfer of Per- and Polyfluoroalkyl Substances (PFASs) in Plants: A New Small-Data Machine Learning Method to... by Yuan Zhang, Yanting Li, Yang Li, Lin Zhao, Yongkui Yang

“…The original training set was expanded nine times by combining the synthetic minority oversampling technique and a variational autoencoder. Subsequently, the four ML models were applied to the test set to predict the selected output parameters. …”
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