Suchergebnisse - "Multimodal Autoencoder"

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals

Autoren: Atik Faysal Taha Boushine Mohammad Rostami et al.

Quelle: IEEE Communications Letters. 29:1659-1663

Schlagwörter: Computer Science - Machine Learning

Zugangs-URL: http://arxiv.org/abs/2501.11538

SecMa: A Novel Multimodal Autoencoder Framework for Encrypted IoT Traffic Analysis and Attack Detection

Autoren: V. Ravi A. S. Poornima

Quelle: Engineering, Technology & Applied Science Research. 15:23020-23026

Phishing Webpage Detection using URL and HTML Graphs based on a Multimodal AutoEncoder Ensemble

Autoren: Jun-Ho Yoon Seok-Hun Choi Hae-Jung Kim et al.

Quelle: Journal of KIISE. 52:461-468

An Attention Fusion-Based Multimodal Autoencoder Deepfake Detection Framework

Autoren: Tong Hu Wei Liu Yaqi Fan et al.

Quelle: 2025 5th International Conference on Neural Networks, Information and Communication Engineering (NNICE). :1030-1034

MOADE: a multimodal autoencoder for dissociating bulk multi-omics data

Autoren: Jiao Sun Ayesha A. Malik Tong Lin et al.

Quelle: Genome Biology. 26

Author Correction: MOADE: a multimodal autoencoder for dissociating bulk multi-omics data

Autoren: Jiao Sun Ayesha A. Malik Tong Lin et al.

Quelle: Genome Biology, Vol 26, Iss 1, Pp 1-3 (2025)

Schlagwörter: Biology (General), QH301-705.5, Genetics, QH426-470

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/1474-760X

Zugangs-URL: https://doaj.org/article/7367d2703b84420e9509980f141b1524

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals

Autoren: Faysal, Atik Boushine, Taha Rostami, Mohammad et al.

Index Begriffe: Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2501.11538

Social-MAE: A Transformer-Based Multimodal Autoencoder for Face and Voice

Autoren: Bohy, Hugo Tran, Minh Haddad, Kevin El et al.

Quelle: 2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG). :1-5

Schlagwörter: FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Vision and Pattern Recognition

Zugangs-URL: http://arxiv.org/abs/2508.17502

Recognizing Bearings’ Degradation Stage Using Multimodal Autoencoder to Learn Features from Different Time Series

Autoren: Alfeo A. L. Cimino M. G. C. A. Gagliardi G.

Quelle: SN Computer Science. 5

Schlagwörter: 0209 industrial biotechnology, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Contrastive learning, Data fusion, Deep feature learning, Multimodal autoencoder, Predictive maintenance, Smart manufacturing

MODE: high-resolution digital dissociation with deep multimodal autoencoder

Autoren: Jiao Sun Ayesha A. Malik Tong Lin et al.

Speech Synthesis from Indonesian Lip Movements Leveraging Bi-Lstm and Attention Mechanisms in Multimodal Autoencoder

Autoren: Eka Rahayu Setyaningsih Anik Nur Handayani Wahyu Sakti Gunawan Irianto et al.

Multimodal Imputation-Based Multimodal Autoencoder Framework for AQI Classification and Prediction of Indian Cities

Autoren: Routhu Srinivasa Rao Lakshmana Rao Kalabarige M. Raviraja Holla et al.

Quelle: IEEE Access, Vol 12, Pp 108350-108363 (2024)

Schlagwörter: PM₂.₅, AQI, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, India, imputation, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, environmental sustainability, 01 natural sciences, PM₁₀, TK1-9971, 0105 earth and related environmental sciences

Zugangs-URL: https://doaj.org/article/53c31c0181df4dbaa80a390ffb261175

Deep multimodal autoencoder for crack criticality assessment

Autoren: Hugo Launay David Ryckelynck Laurent Lacourt et al.

Weitere Verfasser: Hugo Launay David Ryckelynck Laurent Lacourt et al.

Quelle: International Journal for Numerical Methods in Engineering. 123:1456-1480

Schlagwörter: Digital Twin, Finite Element Method, 0202 electrical engineering, electronic engineering, information engineering, Multimodal autoencoder, Transfer Learning, 02 engineering and technology, CNN, [SPI.MAT]Engineering Sciences [physics]/Materials

A deep multimodal autoencoder-decoder framework for customer churn prediction incorporating chat-GPT

Autoren: Yun Li Guoen Xia Su Wang et al.

Quelle: Multimedia Tools and Applications. 83:89563-89589

Abstract 5010: High-resolution multi-omic dissociation of brain tumors with deep multimodal autoencoder

Autoren: Jiao Sun Yue Pan Tong Lin et al.

Quelle: Cancer Research. 85:5010-5010

FM-AE: Frequency-masked Multimodal Autoencoder for Zinc Electrolysis Plate Contact Abnormality Detection

Autoren: Zhou, Canzong Zhou, Can Zhu, Hongqiu et al.

Index Begriffe: Computer Science - Computer Vision and Pattern Recognition, I.5.1, text

URL: http://arxiv.org/abs/2401.03806

Joint ECG–EMG–EEG signal compression and reconstruction with incremental multimodal autoencoder approach

Autoren: Evangelin Dasan Rajakumar Gnanaraj

Quelle: Circuits, Systems, and Signal Processing. 41:6152-6181

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

An Efficient and Explanatory Image and Text Clustering System with Multimodal Autoencoder Architecture

Autoren: Shi, Tiancheng Wei, Yuanchen Kender, John R.

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Computer Science - Multimedia, Multimedia (cs.MM), Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2408.07791

Multimodal Autoencoder Predicts fNIRS Resting State From EEG Signals

Autoren: Dang Khoa Nguyen Frédéric Lesage Parikshat Sirpal et al.

Quelle: Neuroinformatics

Schlagwörter: Brain Mapping, Epilepsy, Spectroscopy, Near-Infrared, Brain, Electroencephalography, EEG-fNIRS, Functional connectivity, 03 medical and health sciences, 0302 clinical medicine, Functional brain imaging, Deep neural networks, Humans, Original Article, Resting state, Neurovascular coupling

Zugangs-URL: https://link.springer.com/content/pdf/10.1007/s12021-021-09538-3.pdf
https://pubmed.ncbi.nlm.nih.gov/34378155
https://link.springer.com/article/10.1007/s12021-021-09538-3
https://link.springer.com/content/pdf/10.1007/s12021-021-09538-3.pdf
https://pubmed.ncbi.nlm.nih.gov/34378155/
http://www.ncbi.nlm.nih.gov/pubmed/34378155

Enhancing Music Recommendation with Social Media Content: an Attentive Multimodal Autoencoder Approach

Autoren: Yan Li Hanjie Wang Jia Jia et al.

Quelle: 2020 International Joint Conference on Neural Networks (IJCNN). :1-8

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2020.html#Shen0LWC20
https://ieeexplore.ieee.org/document/9206894/