Depth-Aware Object Tracking With a Conditional Variational Autoencoder by Wenhui Huang, Jason Gu, Yinchen Guo

“…In this paper, we exploit probabilistic depth-aware object tracking with a conditional variational autoencoder (CVAE). First, we build a bridge between the Siamese network and the variational autoencoder conditioned with depth images and propose a novel multimodal Bayesian object tracking method. …”
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Disentangling Generative Factors of Physical Fields Using Variational Autoencoders by Christian Jacobsen, Karthik Duraisamy

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Variational autoencoders for new physics mining at the Large Hadron Collider by Olmo Cerri, Thong Q. Nguyen, Maurizio Pierini, Maria Spiropulu, Jean-Roch Vlimant

“…Abstract Using variational autoencoders trained on known physics processes, we develop a one-sided threshold test to isolate previously unseen processes as outlier events. …”
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Cost-Sensitive Variational Autoencoding Classifier for Imbalanced Data Classification by Fen Liu, Quan Qian

Subjects: “…variational autoencoder…”
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Gaussian Mixture Variational Autoencoder for Semi-Supervised Topic Modeling by Cangqi Zhou, Hao Ban, Jing Zhang, Qianmu Li, Yinghua Zhang

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Innovative Variational AutoEncoder for an End-to-End Communication System by Mohamad A. Alawad, Mutasem Q. Hamdan, Khairi A. Hamdi

Subjects: “…Variational autoencoder…”
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Underdetermined Source Separation Based on Generalized Multichannel Variational Autoencoder by Shogo Seki, Hirokazu Kameoka, Li Li, Tomoki Toda, Kazuya Takeda

“…To address this limitation, an extension of ILRMA called the multichannel variational autoencoder (MVAE) method was recently proposed, where a conditional VAE (CVAE) is used instead of the NMF model for expressing source power spectrograms. …”
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Deep learning for photovoltaic defect detection using variational autoencoders by Edward J. Westraadt, Warren J. Brettenny, Chantelle M. Clohessy

“…We propose the use of variational autoencoders (VAEs) as a method to artificially expand the data set in order to improve the classification task in this context. …”
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Collaborative Additional Variational Autoencoder for Top-N Recommender Systems by Ming He, Qian Meng, Shaozong Zhang

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PuVAE: A Variational Autoencoder to Purify Adversarial Examples by Uiwon Hwang, Jaewoo Park, Hyemi Jang, Sungroh Yoon, Nam Ik Cho

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A Generation of Enhanced Data by Variational Autoencoders and Diffusion Modeling by Young-Jun Kim, Seok-Pil Lee

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Purify unlearnable examples via rate-constrained variational autoencoders by Yu, Yi, Wang, Yufei, Xia, Song, Yang, Wenhan, Lu, Shijian, Tan, Yap Peng, Kot, Alex Chichung

“…Firstly, we uncover rate-constrained variational autoencoders (VAEs), demonstrating a clear tendency to suppress the perturbations in UEs. …”
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Classification of attention deficit hyperactivity disorder using variational autoencoder by A. Samah, Azurah, Ahmad, Siti Nurul Aqilah, Abdul Majid, Hairudin, Ali Shah, Zuraini, Hashim, Haslina, Azman, Nuraina Syaza, Azmi, Nur Sabrina, Dewi Nasien, Dewi Nasien

“…Past attempts of classifying ADHD based on functional connectivity coefficient using the Deep Neural Network (DNN) result in 95% accuracy. As Variational Autoencoder (VAE) is the most popular in extracting high-level data, this model is applied in this study. …”
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Health Indicator for Low-Speed Axial Bearings Using Variational Autoencoders by Martin Hemmer, Andreas Klausen, Huynh Van Khang, Kjell G. Robbersmyr, Tor I. Waag

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Machine Learning With Variational AutoEncoder for Imbalanced Datasets in Intrusion Detection by Ying-Dar Lin, Zi-Qiang Liu, Ren-Hung Hwang, Van-Linh Nguyen, Po-Ching Lin, Yuan-Cheng Lai

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β-Variational autoencoders and transformers for reduced-order modelling of fluid flows by Alberto Solera-Rico, Carlos Sanmiguel Vila, Miguel Gómez-López, Yuning Wang, Abdulrahman Almashjary, Scott T. M. Dawson, Ricardo Vinuesa

“…Abstract Variational autoencoder architectures have the potential to develop reduced-order models for chaotic fluid flows. …”
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An Efficient Anomaly Detection System for Crowded Scenes Using Variational Autoencoders by Ming Xu, Xiaosheng Yu, Dongyue Chen, Chengdong Wu, Yang Jiang

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Optimization of force-field potential parameters using conditional variational autoencoder by Koki Matsunoshita, Yudai Yamaguchi, Masato Hamaie, Motoki Horibe, Naoto Tanibata, Hayami Takeda, Masanobu Nakayama, Masayuki Karasuyama, Ryo Kobayashi

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An Overview of Variational Autoencoders for Source Separation, Finance, and Bio-Signal Applications by Aman Singh, Tokunbo Ogunfunmi

Subjects: “…variational autoencoders…”
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Spatiotemporal trajectories in resting-state FMRI revealed by convolutional variational autoencoder by Xiaodi Zhang, Eric A. Maltbie, Shella D. Keilholz

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