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

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

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Integrated multi-omics analysis of ovarian cancer using variational autoencoders по 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. по 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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Model Predictive Control with Variational Autoencoders for Signal Temporal Logic Specifications по Eunji Im, Minji Choi, Kyunghoon Cho

“...A robustness margin, quantifying the degree of rule satisfaction, is learned from expert demonstrations using a Conditional Variational Autoencoder (CVAE). This learned margin is then applied in the MPC process to guide the prioritization or exclusion of rules. ...”
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Variational Autoencoders for Cancer Data Integration: Design Principles and Computational Practice по Nikola Simidjievski, Cristian Bodnar, Ifrah Tariq, Ifrah Tariq, Paul Scherer, Helena Andres Terre, Zohreh Shams, Mateja Jamnik, Pietro Liò

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Marked point process variational autoencoder with applications to unsorted spiking activities. по Ryohei Shibue, Tomoharu Iwata

“...To address this limitation, we propose a new joint mark intensity model based on a variational autoencoder, capable of representing the dependency structure of unsorted spikes on observed covariates or hidden states in a data-driven manner. ...”
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Nonparametric Representation of Neutron Star Equation of State Using Variational Autoencoder по 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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Marked point process variational autoencoder with applications to unsorted spiking activities по Ryohei Shibue, Tomoharu Iwata

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Property-guided generation of complex polymer topologies using variational autoencoders по Shengli Jiang, Adji Bousso Dieng, Michael A. Webb

“...Here, we use a generative machine learning model based on variational autoencoders and data generated from molecular dynamics simulations to design polymer topologies that exhibit target properties. ...”
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Representation Learning with a Variational Autoencoder for Predicting Nitrogen Requirement in Rice по Miltiadis Iatrou, Christos Karydas, Xanthi Tseni, Spiros Mourelatos

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Improving Data Generalization With Variational Autoencoders for Network Traffic Anomaly Detection по 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 по 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 по 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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Autonomous design of new chemical reactions using a variational autoencoder по Robert Tempke, Terence Musho

“...Here, the authors develop an artificial intelligence model based on a variational autoencoder to synthetically generate continuous datasets and generate new chemical reactions in a less biased way, by sampling the entirety of the solution space....”
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Degradation assessment for the ball screw with variational autoencoder and kernel density estimation по 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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Automated defect detection in nanomaterial-coated-fabrics using variational autoencoder по Nguyen Ngoc Tram, Kim Jooyong

“...This paper introduces an unsupervised method for detecting regions with a high density of nanomaterials on coated fabric using Variational Autoencoder, a generative model capable of learning dominant features of the input data and generating similar outputs. ...”
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Unsupervised flood detection on SAR time series using variational autoencoder по 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 по 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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Dual Branch Feature Representation and Variational Autoencoder for Panchromatic and Multispectral Classification по Wenping Ma, Runzhe Jing, Hao Zhu, Huanhuan Wu, Yanshan Guo, Xiaoyu Yi, Pute Guo, Yue Wu

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