Search Results - (beta OR bela)-convolutional variational autoencoder*

A robust variational autoencoder using beta divergence by Akrami, Haleh, Joshi, Anand A., Li, Jian, Aydöre, Sergül, Leahy, Richard M.

“…) are not present in the training data. Here we focus on variational autoencoders (VAEs). While the VAE is a popular framework for anomaly detection tasks, we observe…”
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An Unsupervised Approach to Wind Turbine Blade Icing Detection Based on Beta Variational Graph Attention Autoencoder by Wang, Lei, He, Yigang, Shao, Kaixuan, Xing, Zhikai, Zhou, Yazhong

“… To address these shortcomings, the current study proposes a Beta variational graph attention autoencoder (β-VGATAE) for blade ID. The β…”
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β-Variational autoencoders and transformers for reduced-order modelling of fluid flows by Solera-Rico, Alberto, Sanmiguel Vila, Carlos, Gómez-López, Miguel, Wang, Yuning, Almashjary, Abdulrahman, Dawson, Scott T. M., Vinuesa, Ricardo

“…Variational autoencoder architectures have the potential to develop reduced-order models for chaotic fluid flows…”
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Mathematical Modeling of Aβ-42 Dimerization Dynamics: Integrating Physics-Based Simulations, Graph-Based Variational Autoencoder-Driven Neural Relational Inference, and Chaos Theory by Sayyah, Ehsan, Kurul, Emel, Tunç, Hüseyin, Durdağı, Serdar

“…Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by the pathological aggregation of amyloid-beta (Aβ…”
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An Overview of Variational Autoencoders for Source Separation, Finance, and Bio-Signal Applications by Singh, Aman, Ogunfunmi, Tokunbo

“… Variational Autoencoders (VAEs) can be regarded as enhanced Autoencoders where a Bayesian approach is used to learn the probability distribution of the input…”
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Predicting drug polypharmacology from cell morphology readouts using variational autoencoder latent space arithmetic by Chow, Yuen Ler, Singh, Shantanu, Carpenter, Anne E., Way, Gregory P.

“…A variational autoencoder (VAE) is a machine learning algorithm, useful for generating a compressed and interpretable latent space…”
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Elucidating Protein Dynamics through the Optimal Annealing of Variational Autoencoders by Adhikari, Subinoy, Mondal, Jagannath

“… However, unraveling these dynamics remains a formidable challenge. An emerging approach has been to train the conformational ensemble via deep Variational autoencoders (VAEs…”
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Spatial-Frequency-Scale Variational Autoencoder for Enhanced Flow Diagnostics of Schlieren Data by Yang, Ronghua, Wu, Hao, Yang, Rongfei, Wu, Xingshuang, Song, Yifan, Lü, Meiying, Wang, Mingrui

“… In this study, we propose a Spatial-Frequency-Scale variational autoencoder (SFS-VAE), a deep learning framework designed for the unsupervised feature decomposition of Schlieren sensor data…”
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A variational autoencoder trained with priors from canonical pathways increases the interpretability of transcriptome data by Liu, Bin, Rosenhahn, Bodo, Illig, Thomas, DeLuca, David S.

“… that drive transcriptome signals. We investigate whether simple, variational autoencoder (VAE), and beta-weighted VAE are capable of learning reduced representations of transcriptomes that retain critical biological information…”
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TCRCluster: a novel approach to T-cell receptor latent featurization and clustering using contrastive learning-guided two-stage variational autoencoders by Wan, Yat-Tsai Richie, Nielsen, Morten

“… For this, we developed a variational autoencoder (VAE)-based model trained on paired TCR α–β…”
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Anomaly Detection in Time Series with Robust Variational Quasi-Recurrent Autoencoders by Kieu, Tung, Yang, Bin, Guo, Chenjuan, Cirstea, Razvan-Gabriel, Zhao, Yan, Song, Yale, Jensen, Christian S.

“…We propose variational quasi-recurrent autoencoders (VQRAEs) to enable robust and efficient anomaly detection in time series in unsupervised settings…”
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Disentangled representational learning for anomaly detection in single-lead electrocardiogram signals using variational autoencoder by Kapsecker, Maximilian, Möller, Matthias C., Jonas, Stephan M.

“… A beta total correlation variational autoencoder was optimized on four of these datasets for 68 systematic parameterization variants…”
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A dimensionality reduction algorithm for mapping tokamak operational regimes using a variational autoencoder (VAE) neural network by Wei, Y., Levesque, J.P., Hansen, C.J., Mauel, M.E., Navratil, G.A.

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β-Variational autoencoder as an entanglement classifier by Sá, Nahum, Roditi, Itzhak

“… We focus on using an architecture similar to the β-Variational Autoencoder (β-VAE) to discriminate if a quantum state is entangled or separable based on measurements…”
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Source Separation in Joint Communication and Radar Systems Based on Unsupervised Variational Autoencoder by Alaghbari, Khaled A., Lim, Heng Siong, Jin, Benzhou, Shen, Yutong

“…) systems to achieve the required performance, especially at a low signal-to-noise ratio (SNR). In this paper, we propose the use of a generative model, such as the unsupervised variational autoencoder…”
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Quality-related nonlinear process monitoring of power plant by a novel hybrid model based on variational autoencoder by Wang, Peng, Ren, Shaojun, Wang, Yan, Zhu, Baoyu, Fan, Wei, Si, Fengqi

“… In this paper, a novel hybrid model called information bottleneck-based variational autoencoder with supervised regression (IBVAE-SR…”
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Research on multi-heat source arrangement optimization based on equivalent heat source method and reconstructed variational autoencoder by Yang, Yide, Gong, Mali, Ma, Jianshe

“…The variational autoencoder (VAE) architecture has significant advantages in predictive image generation…”
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FVAE: a regularized variational autoencoder using the Fisher criterion by Lai, Jie, Wang, Xiaodan, Xiang, Qian, Li, Rui, Song, Yafei

“…As a deep generative model, the variational autoencoder (VAE) is widely applied to solve problems of insufficient samples and imbalanced labels…”
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Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder by Cao, Shichen, Li, Jingjing, Nelson, Kenric P., Kon, Mark A.

“…We present a coupled variational autoencoder (VAE) method, which improves the accuracy and robustness of the model representation of handwritten numeral images…”
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Learning minimal representations of stochastic processes with variational autoencoders by Fernández-Fernández, Gabriel, Manzo, Carlo, Lewenstein, Maciej, Dauphin, Alexandre, Muñoz-Gil, Gorka

“… Our method builds upon an extended β-variational autoencoder architecture. By means of simulated data sets corresponding to paradigmatic diffusion models, we showcase…”
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