Underdetermined Source Separation Based on Generalized Multichannel Variational Autoencoder di 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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A Variational Autoencoder Mixture Model for Online Behavior Recommendation di Minh-Duc Nguyen, Yoon-Sik Cho

Soggetti: Testo

Deep learning for photovoltaic defect detection using variational autoencoders di 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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Variational Autoencoding with Conditional Iterative Sampling for Missing Data Imputation di Shenfen Kuang, Jie Song, Shangjiu Wang, Huafeng Zhu

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Unsupervised data imputation with multiple importance sampling variational autoencoders di Shenfen Kuang, Yewen Huang, Jie Song

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Variational Autoencoders-Based Algorithm for Multi-Criteria Recommendation Systems di Salam Fraihat, Qusai Shambour, Mohammed Azmi Al-Betar, Sharif Naser Makhadmeh

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Representation learning of resting state fMRI with variational autoencoder di Jung-Hoon Kim, Yizhen Zhang, Kuan Han, Zheyu Wen, Minkyu Choi, Zhongming Liu

Soggetti: “...Variational autoencoder...”
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Double Discrete Variational Autoencoder for Epileptic EEG Signals Classification di Shufeng Liang, Xin Zhang, Hulin Zhao, Yuanyuan Dang, Rui Hui, Jianning Zhang

“...In this paper, we propose a novel Double Discrete Variational AutoEncoder (D2-VAE) network to learn the latent representations in EEG signals and perform deep discretization. ...”
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Collaborative Additional Variational Autoencoder for Top-N Recommender Systems di Ming He, Qian Meng, Shaozong Zhang

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

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Purify unlearnable examples via rate-constrained variational autoencoders di 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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Health Indicator for Low-Speed Axial Bearings Using Variational Autoencoders di Martin Hemmer, Andreas Klausen, Huynh Van Khang, Kjell G. Robbersmyr, Tor I. Waag

Soggetti: Testo

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

Soggetti: Testo

β-Variational autoencoders and transformers for reduced-order modelling of fluid flows di 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 di Ming Xu, Xiaosheng Yu, Dongyue Chen, Chengdong Wu, Yang Jiang

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

Soggetti: Testo

An Effective Pipeline for Training Variational Autoencoders for Synthesizable and Optimized Molecular Design di Fardeen H. Mozumder, Byung-Jun Yoon

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

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

Soggetti: Testo

Interpretation for Variational Autoencoder Used to Generate Financial Synthetic Tabular Data di Jinhong Wu, Konstantinos Plataniotis, Lucy Liu, Ehsan Amjadian, Yuri Lawryshyn

Soggetti: Testo