Search Results - Multi-Modal Variational Autoencoder

MC-RVAE: Multi-channel recurrent variational autoencoder for multimodal Alzheimer’s disease progression modelling by Martí-Juan, Gerard, Lorenzi, Marco, Piella, Gemma

“…•A multi-channel model based on recurrent variational autoencoders was proposed to capture spatial and temporal evolution of AD using multimodal data…”
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Dmvae: a dual-stream multi-modal variational autoencoder for multi-task fake news detection by Guo, Ying, Hu, Shuting, Li, Yao, Di, Chong, Liu, Jie

“…The proliferation of fake news on social media platforms, facilitated by the development of the Internet, has become a pressing social issue, intensifying the urgency of detecting its diverse multi-modal forms…”
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Multimodal variational autoencoder for inverse problems in geophysics: application to a 1-D magnetotelluric problem by Rodriguez, Oscar, Taylor, Jamie M, Pardo, David

“… However, most geophysical applications exhibit more than one plausible solution. Here, we propose a multimodal variational autoencoder model that employs a mixture…”
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Geometry-informed multimodal variational autoencoder for real-time prediction of properties for Ti–6Al–4V fabricated using PBF-LB by Luo, Qixiang, Huang, Nancy, Bartles, Dean L., Beese, Allison M.

“…A geometry-informed multimodal variational autoencoder linear hybrid model (GMVAE) was developed to use in situ processing signals along with geometry information from laser scanning patterns to predict the mechanical properties of Ti-6Al-4…”
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Normative Modeling using Multimodal Variational Autoencoders to Identify Abnormal Brain Volume Deviations in Alzheimer's Disease by Kumar, Sayantan, Payne, Philip R O, Sotiras, Aristeidis

“… To address this limitation, we propose a multi-modal variational autoencoder (mmVAE) based normative modelling framework that can capture the joint distribution between different modalities to identify…”
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Anytime 3D Object Reconstruction Using Multi-Modal Variational Autoencoder by Yu, Hyeonwoo, Oh, Jean

“… In a harsh remote collaboration setting, data compression techniques such as autoencoder can be utilized to obtain and transmit the data in terms of latent variables in a compact form…”
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Multi-Modal Domain Adaptation Variational Autoencoder for EEG-Based Emotion Recognition by Wang, Yixin, Qiu, Shuang, Li, Dan, Du, Changde, Lu, Bao-Liang, He, Huiguang

“… To solve this problem, we propose a multi-modal domain adaptive variational autoencoder (MMDA-VAE) method, which learns shared cross-domain latent representations of the multi-modal data…”
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Bayesian mixture variational autoencoders for multi-modal learning by Liao, Keng-Te, Huang, Bo-Wei, Yang, Chih-Chun, Lin, Shou-De

“…This paper provides an in-depth analysis on how to effectively acquire and generalize cross-modal knowledge for multi-modal learning. Mixture-of-Expert (MoE…”
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Combined Generation of Electrocardiogram and Cardiac Anatomy Models Using Multi-Modal Variational Autoencoders by Beetz, Marcel, Banerjee, Abhirup, Sang, Yuling, Grau, Vicente

“… In this work, we propose a novel multi-modal variational autoencoder (VAE) capable of processing combined physiology and bitemporal anatomy information in the form of electrocardiograms (ECG…”
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Multi-modal hybrid modeling strategy based on Gaussian Mixture Variational Autoencoder and spatial–temporal attention: Application to industrial process prediction by Peng, Haifei, Long, Jian, Huang, Cheng, Wei, Shibo, Ye, Zhencheng

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Bayesian structural model updating with multimodal variational autoencoder by Itoi, Tatsuya, Amishiki, Kazuho, Lee, Sangwon, Yaoyama, Taro

“… The proposed method utilizes the surrogate unimodal encoders of a multimodal variational autoencoder (VAE…”
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Time-Lag Aware Multi-Modal Variational Autoencoder Using Baseball Videos And Tweets For Prediction Of Important Scenes by Hirasawa, Kaito, Maeda, Keisuke, Ogawa, Takahiro, Haseyama, Miki

“…A novel method based on time-lag aware multi-modal variational autoencoder for prediction of important scenes (TI-MVAE-PIS…”
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Explainable Dynamic Multimodal Variational Autoencoder for the Prediction of Patients With Suspected Central Precocious Puberty by Xu, Yiming, Liu, Xiaohong, Pan, Liyan, Mao, Xiaojian, Liang, Huiying, Wang, Guangyu, Chen, Ting

“…), and pelvic ultrasonography and left-hand radiography reports. The challenges are in integrating these multimodal features into a comprehensive deep learning model…”
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Personalized design aesthetic preference modeling: a variational autoencoder and meta-learning approach for multi-modal feature representation and transfer optimization by Chen, Chengliang, Gong, Zhiqun

“…This research presents a comprehensive framework for personalized design aesthetic preference modeling that integrates variational autoencoders (VAE…”
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One-class learning for fake news detection through multimodal variational autoencoders by Gôlo, Marcos Paulo Silva, de Souza, Mariana Caravanti, Rossi, Rafael Geraldeli, Rezende, Solange Oliveira, Nogueira, Bruno Magalhães, Marcacini, Ricardo Marcondes

“…Machine learning methods to detect fake news typically use textual features and Binary or Multi-class classification. However, accurately labeling a large news…”
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Detection of Important Scenes in Baseball Videos via a Time-Lag-Aware Multimodal Variational Autoencoder by Hirasawa, Kaito, Maeda, Keisuke, Ogawa, Takahiro, Haseyama, Miki

“…A new method for the detection of important scenes in baseball videos via a time-lag-aware multimodal variational autoencoder (Tl-MVAE…”
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Multimodal Disentangled Variational Autoencoder With Game Theoretic Interpretability for Glioma Grading by Cheng, Jianhong, Gao, Min, Liu, Jin, Yue, Hailin, Kuang, Hulin, Liu, Jun, Wang, Jianxin

“… In this study, we propose a deep neural network model termed as multimodal disentangled variational autoencoder (MMD-VAE…”
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iMVAN: integrative multimodal variational autoencoder and network fusion for biomarker identification and cancer subtype classification by Dhillon, Arwinder, Singh, Ashima, Bhalla, Vinod Kumar

“…) for biomarker identification and cancer subtype classification. In this research, iMVAN, an integrated Multimodal Variational Autoencoder and Network fusion, is presented for biomarker discovery and classification of cancer subtypes…”
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Improving news recommendation accuracy through multimodal variational autoencoder and adversarial training by Tang, Pei, Zhu, Shuo, Alatas, Bilal

“… A Multimodal Variational Autoencoder (MVAVE) is constructed based on the multi-head self-attention mechanism, incorporating noise injection for denoising to mitigate the impact of data noise on recommendation performance…”
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A multimodal dynamical variational autoencoder for audiovisual speech representation learning by Sadok, Samir, Leglaive, Simon, Girin, Laurent, Alameda-Pineda, Xavier, Séguier, Renaud

“… In particular, the variational autoencoder (VAE) which is equipped with both a generative and an inference model allows for the analysis, transformation, and generation of various types of data…”
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