Anomaly Detection in Asset Degradation Process Using Variational Autoencoder and Explanations by Jakub Jakubowski, Przemysław Stanisz, Szymon Bobek, Grzegorz J. Nalepa

“…The results show that the variational autoencoder slightly outperforms the base autoencoder architecture in anomaly detection tasks. …”
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Evaluating variational autoencoder methods for out-of-distribution detection in autonomous vehicles by Dinh, Phuc Hung

“…OOD detection is a fundamental problem that needs to be addressed to avoid errors in image recognition tasks, especially in real-time system. Variational autoencoder (VAE) has emerged as the most promising method to address this issue. …”
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Out of distribution reasoning by weakly-supervised disentangled logic variational autoencoder by Rahiminasab, Zahra, Yuhas, Michael, Easwaran, Arvind

“…Recently there have been promising results for OOD detection in the latent space of variational autoencoders (VAEs). However, without disentanglement, VAEs cannot perform OOD reasoning. …”
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A Bayesian Nonlinear Reduced Order Modeling Using Variational AutoEncoders by Nissrine Akkari, Fabien Casenave, Elie Hachem, David Ryckelynck

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Modified variational autoencoder for inversely predicting plasmonic nanofeatures for generating structural color by Prajith Pillai, Beena Rai, Parama Pal

“…Abstract We apply a modified variational autoencoder (VAE) regressor for inversely retrieving the topological parameters of the building blocks of plasmonic composites for generating structural colors as per requirement. …”
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DYNAMICAL VARIATIONAL AUTOENCODERS AND KALMANNET: NEW APPROACHES TO ROBUST HIGH-PRECISION NAVIGATION by D. Shen, Y. Ma, G. Liu, J. Hu, Q. Weng, X. Zhu

“…This paper presents a succinct overview of the principles, inference model, and training methodology employed in model-based deep learning methods, with particular focus on the KalmanNet and the Dynamical Variational Autoencoder (DVAE). Furthermore, it implements KalmanNet on robust and high-precision navigation and positioning problem. …”
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PepVAE: Variational Autoencoder Framework for Antimicrobial Peptide Generation and Activity Prediction by Scott N. Dean, Jerome Anthony E. Alvarez, Dan Zabetakis, Scott A. Walper, Anthony P. Malanoski

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The Optimally Designed Variational Autoencoder Networks for Clustering and Recovery of Incomplete Multimedia Data by Xiulan Yu, Hongyu Li, Zufan Zhang, Chenquan Gan

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Graph Variational Autoencoder for Detector Reconstruction and Fast Simulation in High-Energy Physics by Hariri Ali, Dyachkova Darya, Gleyzer Sergei

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Achieving deep clustering through the use of variational autoencoders and similarity-based loss by He Ma

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Diagnosing Faults in Power Transformers With Variational Autoencoder, Genetic Programming, and Neural Network by Juan Ferreira Vidal, Adriana Rosa Garcez Castro

“…To address the imbalance of the data from the database adopted and thus improve the generalization power of the classifier, a data augmentation technique based on a variational autoencoder neural network was used. For the selection and extraction of characteristics from the inputs to the classifier, a technique based on genetic programming (GP) is proposed, which allows the creation of a new n-dimensional space of characteristics, providing a greater ability to increase interclass distances and intraclass compaction. …”
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Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT by Manuel Lopez-Martin, Belen Carro, Antonio Sanchez-Esguevillas, Jaime Lloret

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Optimizing training trajectories in variational autoencoders via latent Bayesian optimization approach by Arpan Biswas, Rama Vasudevan, Maxim Ziatdinov, Sergei V Kalinin

“…Unsupervised and semi-supervised ML methods such as variational autoencoders (VAE) have become widely adopted across multiple areas of physics, chemistry, and materials sciences due to their capability in disentangling representations and ability to find latent manifolds for classification and/or regression of complex experimental data. …”
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Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Autoencoders by Wei Xiong, Neil Yeung, Shubo Wang, Haofu Liao, Liyun Wang, Jiebo Luo

“…We adopt a temporal variational autoencoder (T-VAE) model that utilizes a combination of variational autoencoders and long short-term memory networks to predict bone lesion emergence on our micro-CT dataset containing sequential images of murine tibiae. …”
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Variational autoencoder-based estimation of chronological age and changes in morphological features of teeth by Subin Joo, Won Jung, Seung Eel Oh

“…Abstract This study led to the development of a variational autoencoder (VAE) for estimating the chronological age of subjects using feature values extracted from their teeth. …”
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Variational Autoencoders for chord sequence generation conditioned on Western harmonic music complexity by Luca Comanducci, Davide Gioiosa, Massimiliano Zanoni, Fabio Antonacci, Augusto Sarti

“…More specifically, we consider a pre-existing dataset annotated with the related complexity values and we train two variations of Variational Autoencoders (VAE), namely a Conditional-VAE (CVAE) and a Regressor-based VAE (RVAE), in order to condition the latent space depending on the complexity. …”
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Driving Style-Based Conditional Variational Autoencoder for Prediction of Ego Vehicle Trajectory by Dongchan Kim, Hyukju Shon, Nahyun Kweon, Seungwon Choi, Chanuk Yang, Kunsoo Huh

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Simulating Tariff Impact in Electrical Energy Consumption Profiles With Conditional Variational Autoencoders by Margaux Bregere, Ricardo J. Bessa

“…This paper proposes a novel method based on conditional variational autoencoders (CVAE) to generate, from an electricity tariff profile combined with weather and calendar variables, daily consumption profiles of consumers segmented in different clusters. …”
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Reduced-Complexity End-to-End Variational Autoencoder for on Board Satellite Image Compression by Vinicius Alves de Oliveira, Marie Chabert, Thomas Oberlin, Charly Poulliat, Mickael Bruno, Christophe Latry, Mikael Carlavan, Simon Henrot, Frederic Falzon, Roberto Camarero

“…The aim of this paper is to design a complexity-reduced variational autoencoder in order to meet these constraints while maintaining the performance. …”
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LHC hadronic jet generation using convolutional variational autoencoders with normalizing flows by Breno Orzari, Nadezda Chernyavskaya, Raphael Cobe, Javier Duarte, Jefferson Fialho, Dimitrios Gunopulos, Raghav Kansal, Maurizio Pierini, Thiago Tomei, Mary Touranakou

“…Since the most common final-state objects of high-energy proton collisions are hadronic jets, which are collections of particles collimated in a given region of space, this work aims to develop a convolutional variational autoencoder (ConVAE) for the generation of particle-based LHC hadronic jets. …”
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