Interpretation for Variational Autoencoder Used to Generate Financial Synthetic Tabular Data by Jinhong Wu, Konstantinos Plataniotis, Lucy Liu, Ehsan Amjadian, Yuri Lawryshyn

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Combining variational autoencoders and physical bias for improved microscopy data analysis by Arpan Biswas, Maxim Ziatdinov, Sergei V Kalinin

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Drug-protein interaction prediction via variational autoencoders and attention mechanisms by Yue Zhang, Yuqing Hu, Huihui Li, Xiaoyong Liu

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Integrated multi-omics analysis of ovarian cancer using variational autoencoders by Muta Tah Hira, M. A. Razzaque, Claudio Angione, James Scrivens, Saladin Sawan, Mosharraf Sarkar

“…DL-based dimensionality reduction technique, including variational autoencoder (VAE), is a potential solution to balance high dimensional multi-omics data. …”
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Data augmentation using Variational Autoencoders for improvement of respiratory disease classification. by Jane Saldanha, Shaunak Chakraborty, Shruti Patil, Ketan Kotecha, Satish Kumar, Anand Nayyar

“…This work aims to synthesize respiratory sounds of various categories using variants of Variational Autoencoders like Multilayer Perceptron VAE (MLP-VAE), Convolutional VAE (CVAE) Conditional VAE and compare the influence of augmenting the imbalanced dataset on the performance of various lung sound classification models. …”
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Variational Autoencoders for Cancer Data Integration: Design Principles and Computational Practice by Nikola Simidjievski, Cristian Bodnar, Ifrah Tariq, Ifrah Tariq, Paul Scherer, Helena Andres Terre, Zohreh Shams, Mateja Jamnik, Pietro Liò

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Multichannel Variational Autoencoder-Based Speech Separation in Designated Speaker Order by Lele Liao, Guoliang Cheng, Haoxin Ruan, Kai Chen, Jing Lu

Subjects: “…multichannel variational autoencoder…”
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Unsupervised feature learning for electrocardiogram data using the convolutional variational autoencoder. by Jong-Hwan Jang, Tae Young Kim, Hong-Seok Lim, Dukyong Yoon

“…We propose an unsupervised feature learning method using a convolutional variational autoencoder (CVAE) that can extract ECG features with unlabeled data. …”
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Nonparametric Representation of Neutron Star Equation of State Using Variational Autoencoder by Ming-Zhe Han, Shao-Peng Tang, Yi-Zhong Fan

“…We introduce a new nonparametric representation of the neutron star (NS) equation of state (EOS) by using the variational autoencoder (VAE). As a deep neural network, the VAE is frequently used for dimensionality reduction since it can compress input data to a low-dimensional latent space using the encoder component and then reconstruct the data using the decoder component. …”
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Representation Learning with a Variational Autoencoder for Predicting Nitrogen Requirement in Rice by Miltiadis Iatrou, Christos Karydas, Xanthi Tseni, Spiros Mourelatos

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Improving Data Generalization With Variational Autoencoders for Network Traffic Anomaly Detection by Mehrnoosh Monshizadeh, Vikramajeet Khatri, Marah Gamdou, Raimo Kantola, Zheng Yan

“…To solve the mentioned challenges, we propose a combined architecture comprising a Conditional Variational AutoEncoder (CVAE) and a Random Forest (RF) classifier to automatically learn similarity among input features, provide data distribution in order to extract discriminative features from original features, and finally classify various types of attacks. …”
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Fault Detection Based on Vibration Measurements and Variational Autoencoder-Desirability Function by Rony Ibrahim, Ryad Zemouri, Antoine Tahan, Bachir Kedjar, Arezki Merkhouf, Kamal Al-Haddad

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Rapid Generation of Kilonova Light Curves Using Conditional Variational Autoencoder by Surojit Saha, Michael J. Williams, Laurence Datrier, Fergus Hayes, Matt Nicholl, Albert K. H. Kong, Martin Hendry, IK Siong Heng, Gavin P. Lamb, En-Tzu Lin, Daniel Williams

“…Here, we use a conditional variational autoencoder (CVAE) trained on light-curve data from two kilonova models having different temporal lengths, and consequently, generate kilonova light curves rapidly based on physical parameters of our choice with good accuracy. …”
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Enhancing Conformational Sampling for Intrinsically Disordered and Ordered Proteins by Variational Autoencoder by Jun-Jie Zhu, Ning-Jie Zhang, Ting Wei, Hai-Feng Chen

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Degradation assessment for the ball screw with variational autoencoder and kernel density estimation by Juan Wen, Hongli Gao

“…First, the raw data collected in the normal status are used to train the variational autoencoder, and then, the online raw signals are input into the learned variational autoencoder to construct health indicators. …”
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Unsupervised flood detection on SAR time series using variational autoencoder by Ritu Yadav, Andrea Nascetti, Hossein Azizpour, Yifang Ban

“…The proposed model is based on a spatiotemporal variational autoencoder, trained with reconstruction and contrastive learning techniques. …”
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Improving Variational Autoencoders for New Physics Detection at the LHC With Normalizing Flows by Pratik Jawahar, Thea Aarrestad, Nadezda Chernyavskaya, Maurizio Pierini, Kinga A. Wozniak, Kinga A. Wozniak, Jennifer Ngadiuba, Jennifer Ngadiuba, Javier Duarte, Steven Tsan

“…We investigate how to improve new physics detection strategies exploiting variational autoencoders and normalizing flows for anomaly detection at the Large Hadron Collider. …”
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Robust Multiple-Measurement Sparsity-Aware STAP with Bayesian Variational Autoencoder by Chenxi Zhang, Huiliang Zhao, Wenchao Chen, Bo Chen, Penghui Wang, Changrui Jia, Hongwei Liu

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Rejecting Unknown Gestures Based on Surface-Electromyography Using Variational Autoencoder by Qingfeng Dai, Yongkang Wong, Mohan Kankanhalli, Xiangdong Li, Weidong Geng

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Constructing Dynamic Topic Models Based on Variational Autoencoder and Factor Graph by Zhinan Gou, Lixin Han, Ling Sun, Jun Zhu, Hong Yan

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