MoVAE: Multi-Omics Variational Auto-Encoder for Cancer Subtype Detection by Mohsen Rahmanian, Eghbal G. Mansoori

“…Unlike previous methods, MoVAE effectively handles the integration of heterogeneous data, accommodates missing omics, and manages high-dimensionality. As a variational autoencoder, MoVAE encodes each multi-omics input into a low-dimensional latent space while extracting the omics-dependency and complementary information of multi-omics data according to a prior joint distribution. …”
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DA-LSTM-VAE: Dual-Stage Attention-Based LSTM-VAE for KPI Anomaly Detection by Yun Zhao, Xiuguo Zhang, Zijing Shang, Zhiying Cao

“…Firstly, in order to capture time correlation in KPI data, long–short-term memory (LSTM) units are used to replace traditional neurons in the variational autoencoder (VAE). Then, in order to improve the effect of KPI anomaly detection, an attention mechanism is introduced into the input stage of the encoder and decoder, respectively. …”
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Masked particle modeling on sets: towards self-supervised high energy physics foundation models by Tobias Golling, Lukas Heinrich, Michael Kagan, Samuel Klein, Matthew Leigh, Margarita Osadchy, John Andrew Raine

“…In MPM, particles in a set are masked and the training objective is to recover their identity, as defined by a discretized token representation of a pre-trained vector quantized variational autoencoder. We study the efficacy of the method in samples of high energy jets at collider physics experiments, including studies on the impact of discretization, permutation invariance, and ordering. …”
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DOTA: Deep Learning Optimal Transport Approach to Advance Drug Repositioning for Alzheimer’s Disease by Jacqueline Chyr, Haoran Gong, Xiaobo Zhou

“…Specifically, DOTA consists of two major autoencoders: (1) a multi-modal autoencoder to integrate heterogeneous drug information and (2) a Wasserstein variational autoencoder to identify effective AD drugs. Using our approach, we predict that antipsychotic drugs with circadian effects, such as quetiapine, aripiprazole, risperidone, suvorexant, brexpiprazole, olanzapine, and trazadone, will have efficacious effects in AD patients. …”
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A deep learning latent variable model to identify children with autism through motor abnormalities by Nicola Milano, Roberta Simeoli, Roberta Simeoli, Angelo Rega, Angelo Rega, Davide Marocco

“…In this study, a variational autoencoder, a particular type of Artificial Neural Network, is used to improve ASD detection by analysing the latent distribution description of motion features detected by a tablet-based psychometric scale.ResultsThe proposed ASD detection model revealed that the motion features of children with autism consistently differ from those of children with typical development.DiscussionOur results suggested that it could be possible to identify potential motion hallmarks typical for autism and support clinicians in their diagnostic process. …”
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Jointly efficient encoding and decoding in neural populations. by Simone Blanco Malerba, Aurora Micheli, Michael Woodford, Rava Azeredo da Silveira

“…It takes the form of a variational autoencoder: sensory stimuli are encoded in the noisy activity of neurons to be interpreted by a flexible decoder; encoding must allow for an accurate stimulus reconstruction from neural activity. …”
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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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A semi-supervised learning approach for automated 3D cephalometric landmark identification using computed tomography. by Hye Sun Yun, Chang Min Hyun, Seong Hyeon Baek, Sang-Hwy Lee, Jin Keun Seo

“…The proposed method first detects a small number of easy-to-find reference landmarks, then uses them to provide a rough estimation of the all landmarks by utilizing the low dimensional representation learned by variational autoencoder (VAE). The anonymized landmark dataset is used for training the VAE. …”
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Dirty engineering data-driven inverse prediction machine learning model by Jin-Woong Lee, Woon Bae Park, Byung Do Lee, Seonghwan Kim, Nam Hoon Goo, Kee-Sun Sohn

“…Here we report a novel inverse design strategy that employs two independent approaches: a metaheuristics-assisted inverse reading of conventional forward ML models and an atypical inverse ML model based on a modified variational autoencoder. These two unprecedented approaches were successful and led to overlapped results, from which we pinpointed several novel thermo-mechanically controlled processed (TMCP) steel alloy candidates that were validated by a rule-based thermodynamic calculation tool (Thermo-Calc.). …”
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Enhancing the predictive performance of remote sensing for ecological variables of tidal flats using encoded features from a deep learning model by Logambal Madhuanand, Catharina J. M. Philippart, Jiong Wang, Wiebe Nijland, Steven M. de Jong, Allert I. Bijleveld, Elisabeth A. Addink

“…We tested a novel approach that uses features from a variational autoencoder (VAE) model to enhance the predictive performance of remote sensing images for environmental and ecological variables of tidal flats. …”
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A Bearing Fault Diagnosis Method in Scenarios of Imbalanced Samples and Insufficient Labeled Samples by Xiaohan Cheng, Yuxin Lu, Zhihao Liang, Lei Zhao, Yuandong Gong, Meng Wang

“…First, selective kernel networks (SKNets) and a genetic algorithm (GA) were introduced to construct a conditional variational autoencoder–evolutionary generative adversarial network with a selective kernel (CVAE-SKEGAN) to achieve a balance between the proportion of normal and faulty samples. …”
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Constructing Attention-LSTM-VAE Power Load Model Based on Multiple Features by Chaoyue Ma, Ying Wang, Feng Li, Huiyan Zhang, Yong Zhang, Haiyan Zhang

“…This paper proposes a variational autoencoder (VAE) long short-term memory (LSTM) load model based on the attention mechanism (Attention). …”
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Anomaly VAE-Transformer: A Deep Learning Approach for Anomaly Detection in Decentralized Finance by Ahyun Song, Euiseong Seo, Heeyoul Kim

“…We propose a deep learning model, anomaly VAE-Transformer, which combines the variational autoencoder to extract local information in the short term, and the transformer, to identify dependencies between data in the long term. …”
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Deep Unsupervised Representation Learning for Feature-Informed EEG Domain Extraction by Han Wei Ng, Cuntai Guan

“…This study proposes a novel inference model, the Joint Embedding Variational Autoencoder, that offers conditionally tighter approximation of the estimated spatiotemporal feature distribution through the use of jointly optimised variational autoencoders to achieve optimizable data dependent inputs as an additional variable for improved overall model optimisation and scaling without sacrificing model tightness. …”
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DeepRSSI: Generative Model for Fingerprint-Based Localization by Namkyung Yoon, Wooyong Jung, Hwangnam Kim

“…Focusing on the challenge of extensive labor and time required in traditional data collection, we propose a generative model that combines customized attention mechanism with a conditional variational autoencoder (cVAE), leveraging datasets compiled from direct measurements of RSSI values from different access points in a real-world indoor environment. …”
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Classification of imbalanced ECGs through segmentation models and augmented by conditional diffusion model by Jinhee Kwak, Jaehee Jung

“…To mitigate the pronounced class imbalance in the MIT-BIH arrhythmia dataset, this study employs advanced augmentation techniques, specifically variational autoencoder (VAE) and conditional diffusion, to augment the dataset. …”
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Unsupervised Anomaly Detection for Time Series Data of Spacecraft Using Multi-Task Learning by Kaifei Yang, Yakun Wang, Xiaodong Han, Yuehua Cheng, Lifang Guo, Jianglei Gong

“…First, four proxy tasks are implemented for feature extraction through joint learning: (1) Long short-term memory-based data prediction; (2) autoencoder-based latent representation learning and data reconstruction; (3) variational autoencoder-based latent representation learning and data reconstruction; and (4) joint latent representation-based data prediction. …”
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Generating High-Resolution 3D Faces and Bodies Using VQ-VAE-2 with PixelSNAIL Networks on 2D Representations by Alessio Gallucci, Dmitry Znamenskiy, Yuxuan Long, Nicola Pezzotti, Milan Petkovic

“…Then, we trained a state-of-the-art vector-quantized variational autoencoder (VQ-VAE-2) to learn a latent representation of 2D images and fit a PixelSNAIL autoregressive model to sample novel synthetic meshes. …”
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A State-Supervised Model and Novel Anomaly Index for Gas Turbines Blade Fault Detection Under Multi-Operating Conditions by Yuan Xiao, Kun Feng, Dongyan Miao, Peng Zhang, Jiaxin Yang

“…First, a State-Supervised Variational Autoencoder (SS-VAE) model is introduced, which integrates the learning process of turbine operational states into the VAE bypass, enabling it to capture variations in vibration signal data across different operating conditions. …”
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Analysis of Recommender System Using Generative Artificial Intelligence: A Systematic Literature Review by Matthew O. Ayemowa, Roliana Ibrahim, Muhammad Murad Khan

“…Our systematic review reveals that generative AI models, such as generative adversarial networks (GANs), variational autoencoder (VAEs) and autoencoders have been widely used in recommender systems and they perform better than traditional AI techniques. …”
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