Power System Dispatch Based on Improved Scenario Division with Physical and Data-Driven Features by Wenqi Huang, Shang Cao, Lingyu Liang, Huanming Zhang, Xiangyu Zhao, Hanju Li, Jie Ren, Liang Che

“…To address this issue, this paper proposes an improved scenario division method by integrating the power system’s key physical features and the data-driven variational autoencoder (VAE)-generated features. Next, based on the scenario division results, a multi-scenario data-driven dispatch model is established. …”
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Identification of microbe–disease signed associations via multi-scale variational graph autoencoder based on signed message propagation by Huan Zhu, Hongxia Hao, Liang Yu

“…MSignVGAE employs a graph variational autoencoder to model noisy signed association data and extends the multi-scale concept to enhance representation capabilities. …”
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A distribution information sharing federated learning approach for medical image data by Leiyang Zhao, Jianjun Huang

“…To overcome the performance degradation problem, a novelty distribution information sharing federated learning approach (FedDIS) to medical image classification is proposed that reduce non-IIDness across clients by generating data locally at each client with shared medical image data distribution from others while protecting patient privacy. First, a variational autoencoder (VAE) is federally trained, of which the encoder is uesd to map the local original medical images into a hidden space, and the distribution information of the mapped data in the hidden space is estimated and then shared among the clients. …”
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Robust Authentication Analysis of Copyright Images through Deep Hashing Models with Self-supervision by Jaeyoung Yang, Sooin Kim, Sangwoo Lee, Won-gyum Kim, Donghoon Kim, Doosung Hwang

“…The proposed model is based on an autoencoder or variational autoencoder model and is improved by including convolutional filters, residual blocks, and vision transformer blocks. …”
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Improved downstream functional analysis of single-cell RNA-sequence data using DGAN by Diksha Pandey, Perumal P. Onkara

“…In essence, DGAN is an evolved variational autoencoder designed to robustly impute data dropouts in scRNA-seq data manifested as a sparse gene expression matrix. …”
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Privacy‐preserving generative framework for images against membership inference attacks by Ruikang Yang, Jianfeng Ma, Yinbin Miao, Xindi Ma

“…The framework generates synthetic data satisfying differential privacy through the variational autoencoder model's information extraction and data generation capabilities to improve model accuracy. …”
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Data-driven discovery of 2D materials by deep generative models by Peder Lyngby, Kristian Sommer Thygesen

“…Here, we show that a crystal diffusion variational autoencoder (CDVAE) is capable of generating two-dimensional (2D) materials of high chemical and structural diversity and formation energies mirroring the training structures. …”
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Deep Multi-Task Learning for an Autoencoder-Regularized Semantic Segmentation of Fundus Retina Images by Ge Jin, Xu Chen, Long Ying

“…The shared image encoder is regularized by conducting the reconstruction task in the VQ-VAE (Vector Quantized Variational AutoEncoder) module branch to improve the generalization ability. …”
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Latent variable models for understanding user behavior in software applications by Saeedi, Ardavan

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Out-of-distribution lane detector on a low-cost cyber-physical AV test bed by Gan, Shyan

“…Deep neural network based Variational Autoencoder (VAE) detectors are especially promising on CPS with limited computational resources which also require short inference times Computer vision based lane-following algorithms are well established in the field of autonomous vehicles. …”
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DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents by Lee, N, Choi, W, Vernaza, P, Choy, C, Torr, P, Chandraker, M

“…The model first obtains a diverse set of hypothetical future prediction samples employing a conditional variational autoencoder, which are ranked and refined by the following RNN scoring-regression module. …”

Toward enabling cardiac digital twins of myocardial infarction using deep computational models for inverse inference by Li, L, Camps, J, Wang, Z, Beetz, M, Banerjee, A, Rodriguez, B, Grau, V

“…We subsequently present a novel deep computational model, comprising a dual-branch variational autoencoder and an inference model, to infer infarct location and distribution from the simulated QRS. …”

DiffuSeg: domain-driven diffusion for medical image segmentation by Zhang, L, Wu, F, Bronik, K, Papiez, BW

“…To learn the target domain knowledge, a feature factorization variational autoencoder is proposed to provide conditional information for the diffusion model. …”

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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Early warning signals of failures in building management systems by Mesa-Jiménez José Joaquín, Stokes Lee, Yang QingPing, Livina Valerie

“…For such purpose, variance, lag-1 autocorrelation function (ACF1), power spectrum (PS) and variational autoencoder (VAE) techniques are applied to both univariate and multivariate scenarios. …”
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OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing by Zehua Zeng, Yuqing Ma, Lei Hu, Bowen Tan, Peng Liu, Yixuan Wang, Cencan Xing, Yuanyan Xiong, Hongwu Du

“…Here, we introduce the single cell trajectory blending from Bulk RNA-seq (BulkTrajBlend) algorithm, a component of the OmicVerse suite that leverages a Beta-Variational AutoEncoder for data deconvolution and graph neural networks for the discovery of overlapping communities. …”
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Privacy-Oriented Manipulation of Speaker Representations by Francisco Teixeira, Alberto Abad, Bhiksha Raj, Isabel Trancoso

“…In this work, we propose a method for removing and manipulating private attribute information in speaker representations that leverages a Vector-Quantized Variational Autoencoder architecture combined with an adversarial classifier and a novel mutual information loss. …”
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Joint embedding of structure and features via graph convolutional networks by Sébastien Lerique, Jacob Levy Abitbol, Márton Karsai

“…Building on the recent developments of Graph Convolutional Networks (GCN), we develop a multitask GCN Variational Autoencoder where different dimensions of the generated embeddings can be dedicated to encoding feature information, network structure, and shared feature-network information. …”
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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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Analysis of Residual Current Flows in Inverter Based Energy Systems Using Machine Learning Approaches by Holger Behrends, Dietmar Millinger, Werner Weihs-Sedivy, Anže Javornik, Gerold Roolfs, Stefan Geißendörfer

“…The investigation shows that faults in a photovoltaic converter system cause a unique behaviour of the residual current and fault patterns can be detected and identified by using pattern recognition and variational autoencoder machine learning algorithms. In this context, it was found that the residual current is not only affected by malfunctions of the system, but also by volatile external influences. …”
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