Search Results - Multi-modal 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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PathME: pathway based multi-modal sparse autoencoders for clustering of patient-level multi-omics data by Lemsara, Amina, Ouadfel, Salima, Fröhlich, Holger

“… Results We propose a multi-modal sparse denoising autoencoder framework coupled with sparse non-negative matrix factorization to robustly cluster patients based on multi-omics data…”
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Representation learning using step-based deep multi-modal autoencoders by Bhatt, Gaurav, Jha, Piyush, Raman, Balasubramanian

“… of ‘canonical correlation-based’ approaches and ‘autoencoder-based’ approaches. In this paper, we investigate the performance of deep autoencoder-based methods on multi-view data…”
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Multi-modal semantic autoencoder for cross-modal retrieval by Wu, Yiling, Wang, Shuhui, Huang, Qingming

“… In this paper, we propose a two-stage learning method to learn multi-modal mappings that project multi-modal data to low dimensional embeddings that preserve both feature and semantic information…”
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Thermal-visible stereo matching at night based on Multi-Modal Autoencoder by Zhang, Quan, Li, Yiran, Yang, Le, Zhang, Yi, Li, Zechao, Chen, Xiaoyu, Han, Jing

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A novel transformer autoencoder for multi-modal emotion recognition with incomplete data by Cheng, Cheng, Liu, Wenzhe, Fan, Zhaoxin, Feng, Lin, Jia, Ziyu

“…Multi-modal signals have become essential data for emotion recognition since they can represent emotions more comprehensively…”
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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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Robust anomaly detection through multi-modal autoencoder fusion for small vehicle damage detection by Khan, Sara, Yüksel, Mehmed, Kirchner, Frank

“… This work introduces a novel multi-modal architecture based on anomaly detection to address these issues…”
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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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Multi-Modal masked autoencoder and parallel Mamba for 3D brain tumor segmentation by Huang, Yaya, Liu, Litong, Zhang, Tianzhen, Wang, Sisi, Ting, Chee-Ming

“…Accurate segmentation of brain tumors from multimodal MRI is essential for diagnosis and treatment planning…”
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A swarm anomaly detection model for IoT UAVs based on a multi-modal denoising autoencoder and federated learning by Lu, Yu, Yang, Tao, Zhao, Chong, Chen, Wen, Zeng, Rong

“…•A multimodal denoising autoencoder has been designed to achieve multi-source heterogeneous sensor noise removal…”
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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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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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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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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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ZMGA: A ZINB-based multi-modal graph autoencoder enhancing topological consistency in single-cell clustering by Yao, Jiaxi, Li, Lin, Xu, Tong, Sun, Yang, Jing, Hongwei, Wang, Chengyuan

“… To address these challenges, we introduce a topologically consistent multi-modal graph autoencoder…”
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MMDAE-HGSOC: A novel method for high-grade serous ovarian cancer molecular subtypes classification based on multi-modal deep autoencoder by Wang, Hui-Qing, Li, Hao-Lin, Han, Jia-Le, Feng, Zhi-Peng, Deng, Hong-Xia, Han, Xiao

“… In this paper, we propose a multi-modal deep autoencoder learning method, MMDAE-HGSOC. MiRNA expression, DNA methylation, and copy number variation…”
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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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