Search Results - multimodal 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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Joint ECG–EMG–EEG signal compression and reconstruction with incremental multimodal autoencoder approach by Dasan, Evangelin, Gnanaraj, Rajakumar

“…–EEG signals before sending to the receiver. This work proposes multimodal deep denoising convolutional autoencoder architecture for joint compression (encoding…”
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Multimodal Imputation-Based Multimodal Autoencoder Framework for AQI Classification and Prediction of Indian Cities by Srinivasa Rao, Routhu, Rao Kalabarige, Lakshmana, Holla, M. Raviraja, Kumar Sahu, Aditya

“…) with the features of multi-modal autoencoder (MMA) and fed to an XGBoost(XGB) algorithm for AQI prediction and classification…”
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Disentangled-Multimodal Adversarial Autoencoder: Application to Infant Age Prediction With Incomplete Multimodal Neuroimages by Hu, Dan, Zhang, Han, Wu, Zhengwang, Wang, Fan, Wang, Li, Smith, J. Keith, Lin, Weili, Li, Gang, Shen, Dinggang

“… analysis extremely challenging. With the conventional multimodal fusion strategies, adding fMRI data for age prediction has a high risk of introducing more noises than useful features, which would lead to reduced accuracy…”
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MOADE: a multimodal autoencoder for dissociating bulk multi-omics data by Sun, Jiao, Malik, Ayesha A., Lin, Tong, Bratton, Ayla, Pan, Yue, Smith, Kyle, Onar-Thomas, Arzu, Robinson, Giles W., Zhang, Wei, Northcott, Paul A., Li, Qian

“… Here, we present MOADE, a multimodal autoencoder pipeline linking multi-dimensional features to jointly predict personalized multi-omic profiles and cellular compositions, using pseudo-bulk data…”
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Author Correction: MOADE: a multimodal autoencoder for dissociating bulk multi-omics data by Sun, Jiao, Malik, Ayesha A., Lin, Tong, Bratton, Ayla, Pan, Yue, Smith, Kyle, Onar-Thomas, Arzu, Robinson, Giles W., Zhang, Wei, Northcott, Paul A., Li, Qian

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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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Multimodal geometric AutoEncoder (MGAE) for rail fasteners tightness evaluation with point clouds & monocular depth fusion by Qiu, Shi, Zaheer, Qasim, Muhammad Ahmed Hassan Shah, S., Faizan Hussain Shah, Syed, Ehsan, Haleema, Atta, Zunaira, Ai, Chengbo, Wang, Jin, Wang, Weidong, Peng, Jun

“…•Introduces a Multimodal Geometric AutoEncoder (MGAE) for rail fastener tightness evaluation…”
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AMFN: Autoencoder-led Multimodal Fusion Network for EEG–fNIRS Classification by Bunterngchit, Chayut, Wang, Jiaxing, Su, Jianqiang, Wang, Yihan, Liu, Shiqi, Hou, Zeng-Guang

“… To address these issues, this study introduces the autoencoder-led multimodal fusion network (AMFN) for EEG…”
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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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DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals by Faysal, Atik, Boushine, Taha, Rostami, Mohammad, Roshan, Reihaneh Gh, Wang, Huaxia, Muralidhar, Nikhil, Sahoo, Avimanyu, Yao, Yu-Dong

“…We propose Denoising Masked Autoencoder (DenoMAE), a novel multimodal autoencoder framework for denoising modulation signals during pretraining…”
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Multimodal Deep Autoencoder for Human Pose Recovery by Hong, Chaoqun, Yu, Jun, Wan, Jian, Tao, Dacheng, Wang, Meng

“… It is based on feature extraction with multimodal fusion and back-propagation deep learning…”
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Deep supervised multimodal semantic autoencoder for cross‐modal retrieval by Tian, Yu, Yang, Wenjing, Liu, Qingsong, Yang, Qiong

“… We finally obtain the feature‐aware and content‐aware projections via multimodal semantic autoencoders…”
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Deep multimodal autoencoder for crack criticality assessment by Launay, Hugo, Ryckelynck, David, Lacourt, Laurent, Besson, Jacques, Mondon, Arnaud, Willot, Francois

“… A multimodal autoencoder enables to mix morphological and mechanical data. It contains a single…”
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Deep Multimodal Fusion Autoencoder for Saliency Prediction of RGB-D Images by Huang, Kengda, Zhou, Wujie, Fang, Meixin

“… In this study, we propose a novel deep multimodal fusion autoencoder for the saliency prediction of RGB-D images…”
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Multimodal Autoencoder Predicts fNIRS Resting State From EEG Signals by Sirpal, Parikshat, Damseh, Rafat, Peng, Ke, Nguyen, Dang Khoa, Lesage, Frédéric

“…) and functional near-infrared spectroscopy (fNIRS) recordings from 40 epileptic patients. Long short-term memory units and convolutional neural networks are integrated within a multimodal sequence-to-sequence autoencoder…”
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AutoEncoder-Driven Multimodal Collaborative Learning for Medical Image Synthesis by Cao, Bing, Bi, Zhiwei, Hu, Qinghua, Zhang, Han, Wang, Nannan, Gao, Xinbo, Shen, Dinggang

“… To address this issue, we propose an autoencoder-driven multimodal collaborative learning framework for medical image synthesis…”
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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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A deep multimodal autoencoder-decoder framework for customer churn prediction incorporating chat-GPT by Li, Yun, Xia, Guoen, Wang, Su, Li, Ying

“… Specifically, to better deal with the heterogeneity and consistency problems in the acquired multimodal data, in this paper we propose a multimodal autoencoder-decoder framework for customer churn…”
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