Marked point process variational autoencoder with applications to unsorted spiking activities. by 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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Trust Model for Underwater Wireless Sensor Networks Based on Variational Autoencoders by Jiahao ZHANG, Sizhou WEI, Na XIA

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ECG Sensor Design Assessment with Variational Autoencoder-Based Digital Watermarking by Chih-Yu Hsu, Chih-Yin Chang, Yin-Chi Chen, Jasper Wu, Shuo-Tsung Chen

Subjects: “…variational AutoEncoder…”
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Unsupervised Anomaly Detection With Variational Autoencoders Applied to Full‐Disk Solar Images by Marius Giger, André Csillaghy

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Towards prediction of pseudo-normal SPECT image data using variational autoencoder by Katerina Dudasova, Jiri Trnka

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Deep Learning‐Based Variational Autoencoder for Classification of Quantum and Classical States of Light by Mahesh Bhupati, Abhishek Mall, Anshuman Kumar, Pankaj K. Jha

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Single-cell RNA-Seq Clustering Based on Dual Autoencoder with Variational Bayes by JIA Jihua, XU Yaokui, WANG Minghui

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Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders. by Matthew R Whiteway, Dan Biderman, Yoni Friedman, Mario Dipoppa, E Kelly Buchanan, Anqi Wu, John Zhou, Niccolò Bonacchi, Nathaniel J Miska, Jean-Paul Noel, Erica Rodriguez, Michael Schartner, Karolina Socha, Anne E Urai, C Daniel Salzman, International Brain Laboratory, John P Cunningham, Liam Paninski

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Variational autoencoder-based model improves polygenic prediction in blood cell traits by Xiaoqi Li, Elena Kharitonova, Minxing Pang, Jia Wen, Laura Y. Zhou, Laura Raffield, Haibo Zhou, Huaxiu Yao, Can Chen, Yun Li, Quan Sun

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Using Variational Autoencoders for Out of Distribution Detection in Histological Multiple Instance Learning by Francisco Javier Saez-Maldonado, Luz Garcia, Lee A. D. Cooper, Jeffery A. Goldstein, Rafael Molina, Aggelos K. Katsaggelos

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Failure Detection in Sensors via Variational Autoencoders and Image-Based Feature Representation by Luis Miguel Moreno Haro, Adaiton Oliveira-Filho, Bruno Agard, Antoine Tahan

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Quantum Variational Autoencoder Based on Weak Measurements With Fuzzy Filtering of Input Data by Vyacheslav Korolyov, Maksim Ogurtsov, Oleksandr Khodsinskyi

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Improving News Recommendation Accuracy Through Multimodal Variational Autoencoder and Adversarial Training by Pei Tang, Shuo Zhu, Bilal Alatas

Subjects: “…Variational autoencoder…”
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V2Coder: A Non-Autoregressive Vocoder Based on Hierarchical Variational Autoencoders by Takato Fujimoto, Kei Hashimoto, Yoshihiko Nankaku, Keiichi Tokuda

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Variational Autoencoder-Based Multiobjective Topology Optimization of Electrical Machines Using Vector Graphics by Michael Heroth, Helmut C. Schmid, Magda Gregorova, Rainer Herrler, Wilfried Hofmann

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Harnessing dual variational autoencoders to decode microbe roles in diseases for traditional medicine discovery by Qing Ye, Yaxin Sun, Yaxin Sun, Yaxin Sun

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DVAEGMM: Dual Variational Autoencoder With Gaussian Mixture Model for Anomaly Detection on Attributed Networks by Wasim Khan, Mohammad Haroon, Ahmad Neyaz Khan, Mohammad Kamrul Hasan, Asif Khan, Umi Asma Mokhtar, Shayla Islam

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Variational Autoencoder Based Anomaly Detection in Large-Scale Energy Storage Power Stations by Tuo Ji, Pinghu Xu, Dongliang Guo, Lei Sun, Kangji Ma, Yanan Wang, Xuebing Han

“…This study employs an unsupervised deep learning model based on variational autoencoders (VAEs) to perform anomaly detection on real operational data. …”
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Variational autoencoders for at-source data reduction and anomaly detection in high energy particle detectors by Alexander Yue, Haoyi Jia, Julia Gonski

“…To address these challenges, machine learning in readout electronics can be leveraged for smart detector designs, enabling intelligent inference and data reduction at-source. Variational autoencoders (VAEs) offer a variety of benefits for front-end readout; an on-sensor encoder can perform efficient lossy data compression while simultaneously providing a latent space representation that can be used for anomaly detection. …”
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Into the latent space of capacitive sensors: interpolation and synthetic data generation using variational autoencoders by Miguel Monteagudo Honrubia, Francisco Javier Herraiz-Martínez, Javier Matanza Domingo

Subjects: “…variational autoencoders…”
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