Variational Autoencoder Modular Bayesian Networks for Simulation of Heterogeneous Clinical Study Data by Luise Gootjes-Dreesbach, Meemansa Sood, Meemansa Sood, Akrishta Sahay, Martin Hofmann-Apitius, Martin Hofmann-Apitius, Holger Fröhlich, Holger Fröhlich, Holger Fröhlich

“…In this work, we propose a new machine learning approach [Variational Autoencoder Modular Bayesian Network (VAMBN)] to learn a generative model of longitudinal clinical study data. …”
Get full text

Extracting a biologically latent space of lung cancer epigenetics with variational autoencoders by Zhenxing Wang, Yadong Wang

Subjects: Get full text

Fault Detection and Diagnosis in Industrial Processes with Variational Autoencoder: A Comprehensive Study by Jinlin Zhu, Muyun Jiang, Zhong Liu

Subjects: Get full text

Fault detection and diagnosis in industrial processes with variational autoencoder: a comprehensive study by Zhu, Jinlin, Jiang, Muyun, Liu, Zhong

“…This work considers industrial process monitoring using a variational autoencoder (VAE). As a powerful deep generative model, the variational autoencoder and its variants have become popular for process monitoring. …”
Get full text

PriorVAE: encoding spatial priors with variational autoencoders for small-area estimation by Semenova, ES, Xu, Y, Howes, A, Rashid, T, Bhatt, S, Mishra, S, Flaxman, S

“…Here, we propose a novel, deep generative modelling approach to tackle this challenge, termed PriorVAE: for a particular spatial setting, we approximate a class of GP priors through prior sampling and subsequent fitting of a variational autoencoder (VAE). Given a trained VAE, the resultant decoder allows spatial inference to become incredibly efficient due to the low dimensional, independently distributed latent Gaussian space representation of the VAE. …”

Semi-supervised novelty detection in opportunistic science missions using variational autoencoders by Sintini, L, Kunze, L

“…In this paper, we have designed, developed, and evaluated unsupervised as well as semi-supervised approaches to novelty detection based on Variational Autoencoders (VAE). Our VAE model was trained on typical data from previous missions and tested to infer the novelty of scientific targets. …”

PCGen: A Fully Parallelizable Point Cloud Generative Model by Nicolas Vercheval, Remco Royen, Adrian Munteanu, Aleksandra Pižurica

Subjects: Get full text

Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data by María Teresa García-Ordás, José Alberto Benítez-Andrades, Isaías García-Rodríguez, Carmen Benavides, Héctor Alaiz-Moretón

Subjects: Get full text

An Effective Method for Detecting Unknown Types of Attacks Based on Log-Cosh Variational Autoencoder by Li Yu, Liuquan Xu, Xuefeng Jiang

Subjects: Get full text

Credit Card Fraud Detection Based on Improved Variational Autoencoder Generative Adversarial Network by Yuanming Ding, Wei Kang, Jianxin Feng, Bo Peng, Anna Yang

Subjects: Get full text

Multimode Process Monitoring Based on Modified Density Peak Clustering and Parallel Variational Autoencoder by Feng Yu, Jianchang Liu, Dongming Liu

Subjects: Get full text

VAE-Sim: A Novel Molecular Similarity Measure Based on a Variational Autoencoder by Soumitra Samanta, Steve O’Hagan, Neil Swainston, Timothy J. Roberts, Douglas B. Kell

Subjects: Get full text

An Integrated Framework Based on Latent Variational Autoencoder for Providing Early Warning of At-Risk Students by Xu Du, Juan Yang, Jui-Long Hung

Subjects: Get full text

Versatile domain mapping of scanning electron nanobeam diffraction datasets utilising variational autoencoders by A. Bridger, W. I. F. David, T. J. Wood, M. Danaie, K. T. Butler

“…The workflow utilises a Variational AutoEncoder to identify the sources of variance in the diffraction signal, and this, in combination with clustering techniques, is used to produce domain maps. …”
Get full text

Accurate Tumor Subtype Detection with Raman Spectroscopy via Variational Autoencoder and Machine Learning by Chang He, Shuo Zhu, Xiaorong Wu, Jiale Zhou, Yonghui Chen, Xiaohua Qian, Jian Ye

Get full text

Improving Generative and Discriminative Modelling Performance by Implementing Learning Constraints in Encapsulated Variational Autoencoders by Wenjun Bai, Changqin Quan, Zhi-Wei Luo

“…To demonstrate the usage of these learning constraints, we introduce a novel deep generative model: encapsulated variational autoencoders (EVAEs) to stack two different variational autoencoders together with their learning algorithm. …”
Get full text

The Recommendation Algorithm Based on Improved Conditional Variational Autoencoder and Constrained Probabilistic Matrix Factorization by Yunfei Zhang, Hongzhen Xu, Xiaojun Yu

Subjects: Get full text

Brain Tumor Classification Using a Combination of Variational Autoencoders and Generative Adversarial Networks by Bilal Ahmad, Jun Sun, Qi You, Vasile Palade, Zhongjie Mao

Subjects: “…variational autoencoder…”
Get full text

Application of Variational AutoEncoder (VAE) Model and Image Processing Approaches in Game Design by Hugo Wai Leung Mak, Runze Han, Hoover H. F. Yin

Subjects: Get full text

Unsupervised Seismic Facies Deep Clustering Via Lognormal Mixture-Based Variational Autoencoder by Haowei Hua, Feng Qian, Gulan Zhang, Yuehua Yue

Subjects: “…lognormal mixture-based variational autoencoder (LMVAE)…”
Get full text