Suchergebnisse - "Cross-modal autoencoder"

A Cross-Modal Autoencoder for Contactless Electrocardiography Monitoring Using Frequency-Modulated Continuous Wave Radar

Autoren: Kai-Chun Liu Sheng-Yu Peng Yu Tsao et al.

Quelle: IEEE Sensors Journal. 24:41462-41473

AutoSen: Improving Automatic WiFi Human Sensing through Cross-Modal Autoencoder

Autoren: Gao, Qian Hao, Yanling Liu, Yuanwei

Quelle: ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :8235-8239

Schlagwörter: Computer Science - Machine Learning, Computer Science - Human-Computer Interaction, Electrical Engineering and Systems Science - Signal Processing

Zugangs-URL: http://arxiv.org/abs/2401.05440

Unsupervised Sim-to-Real Adaptation of Soft Robot Proprioception Using a Dual Cross-Modal Autoencoder

Autoren: Chaeree Park Hyunkyu Park Jung Kim

Quelle: Soft Robotics.

Schlagwörter: FOS: Computer and information sciences, Computer Science - Robotics, Robotics (cs.RO)

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/39761056
http://arxiv.org/abs/2310.14075

Convolutional Cross-Modal Autoencoder-Based Few-Shot Learning for Data Augmentation with Application to Alzheimer Dementia Diagnosis

Autoren: Othmane Bazine Omar Rai Oussama Aiadi et al.

Quelle: Cognitive Computation. 17

Cross-modal autoencoder framework learns holistic representations of cardiovascular state

Autoren: Adityanarayanan Radhakrishnan Sam F. Friedman Shaan Khurshid et al.

Quelle: Nature Communications, Vol 14, Iss 1, Pp 1-12 (2023)

Schlagwörter: Science

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2041-1723

Zugangs-URL: https://doaj.org/article/b40d79696a384ecd99367367a11a9949

Unsupervised Sim-to-Real Adaptation of Soft Robot Proprioception using a Dual Cross-modal Autoencoder

Autoren: Park, Chaeree Park, Hyunkyu Kim, Jung

Index Begriffe: Computer Science - Robotics, text

URL: http://arxiv.org/abs/2310.14075

A Cross-Modal Autoencoder Framework Learns Holistic Representations of Cardiovascular State

Autoren: Adityanarayanan Radhakrishnan Sam F. Friedman Shaan Khurshid et al.

Quelle: Nat Commun
Nature Communications, Vol 14, Iss 1, Pp 1-12 (2023)

Schlagwörter: 0301 basic medicine, Electrocardiography, 0303 health sciences, 03 medical and health sciences, Science, Learning, Heart, Cardiovascular System, Article, Genome-Wide Association Study

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/37105979
https://doaj.org/article/b40d79696a384ecd99367367a11a9949

Convolutional Cross-Modal Autoencoder-Based Few-Shot Learning for Data Augmentation with Application to Alzheimer Dementia Diagnosis.

Autoren: Bazine, Othmane Rai, Omar Aiadi, Oussama et al.

Quelle: Cognitive Computation; Feb2025, Vol. 17 Issue 1, p1-14, 14p

Cross-modal autoencoder framework learns holistic representations of cardiovascular state.

Autoren: Radhakrishnan, Adityanarayanan Friedman, Sam F. Khurshid, Shaan et al.

Quelle: Nature Communications; 4/29/2023, Vol. 14 Issue 1, p1-12, 12p

Schlagwörter: CARDIAC magnetic resonance imaging, HOLISTIC education, GENOME-wide association studies, PHENOTYPES

Reports from National Taiwan University of Science and Technology Provide New Insights into Obesity, Fitness and Wellness (A Cross-modal Autoencoder for Contactless Electrocardiography Monitoring Using Frequency-modulated Continuous Wave Radar).

Quelle: Cardiovascular Week; 3/3/2025, p784-784, 1p

Schlagwörter: CONTINUOUS wave radar, AUTOENCODERS, JOURNALISTIC reporting, ELECTRICAL engineering, DEEP learning

New Science Research from Massachusetts Institute of Technology Described (Cross-modal autoencoder framework learns holistic representations of cardiovascular state).

Quelle: Cardiovascular Week; 5/15/2023, p430-430, 1p

Schlagwörter: HOLISTIC education, TECHNICAL institutes, AUTOETHNOGRAPHY, CARDIAC magnetic resonance imaging

Firma/Körperschaft: MASSACHUSETTS Institute of Technology

Integrative Multi-Omics and Artificial Intelligence: A New Paradigm for Systems Biology.

Autoren: Kant, Shashi Deepika Roy, Saheli

Quelle: OMICS: A Journal of Integrative Biology; Dec2025, Vol. 29 Issue 12, p576-587, 12p

Comparison of mass spectrometry and fourier transform infrared spectroscopy of plasma samples in identification of patients with fracture-related infections.

Autoren: Malek, Sarah Natoli, Roman M. Rajwa, Bartek

Quelle: PLoS ONE; 9/22/2025, Vol. 20 Issue 9, p1-16, 16p

Schlagwörter: LIQUID chromatography-mass spectrometry, PLASMA spectroscopy, FOURIER transform infrared spectroscopy, INVASIVE diagnosis, RECEIVER operating characteristic curves

Parametric art creation platform design based on visual delivery and multimedia data fusion.

Autoren: Yun, Qing

Quelle: PeerJ Computer Science; Sep2025, p1-23, 23p

Schlagwörter: VISUAL communication, DEEP learning, MULTISENSOR data fusion, COMPUTER art, FEATURE extraction, MULTIMEDIA communications

Deep learning in chromatin organization: from super-resolution microscopy to clinical applications.

Autoren: Rotkevich, Mikhail Viana, Carlotta Neguembor, Maria Victoria et al.

Quelle: Cellular & Molecular Life Sciences; 8/29/2025, Vol. 82 Issue 1, p1-24, 24p

Schlagwörter: DEEP learning, CHROMATIN, INDIVIDUALIZED medicine, CHROMOSOME structure, HIGH resolution imaging, DIAGNOSTIC services, IMAGE segmentation, IMAGE reconstruction

Topologically Consistent Prototype Network for Incomplete Multimodal Learning.

Autoren: Wang, Yang Zhang, Xu-Yao Liu, Cheng-Lin

Quelle: Cognitive Computation; Jun2025, Vol. 17 Issue 3, p1-18, 18p

非言語モダリティへの言語性付与による感情推定. (Japanese)

Alternate Title: Word-Aware Modality Stimulation for Multimodal Fusion. (English)

Autoren: 立石 修平 大杉 康仁 中辻 真

Quelle: Transactions of the Japanese Society for Artificial Intelligence; 2025, Vol. 40 Issue 3, p1-10, 10p

Schlagwörter: LANGUAGE models, LINGUISTIC context, MODALITY (Linguistics), SENTIMENT analysis, TASK analysis

State of the Art of Artificial Intelligence in Clinical Electrophysiology in 2025: A Scientific Statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), and the ESC Working Group on E-Cardiology Open Access.

Autoren: Svennberg, Emma Han, Janet K Caiani, Enrico G et al.

Quelle: EP: Europace; May2025, Vol. 27 Issue 5, p1-19, 19p

NRAFN: a non-text reinforcement and adaptive fusion network for multimodal sentiment analysis.

Autoren: Wei, Jinlong Shao, Xinhui

Quelle: Multimedia Tools & Applications; Apr2025, Vol. 84 Issue 13, p12013-12035, 23p

Schlagwörter: SENTIMENT analysis, ARTIFICIAL intelligence, VIDEO excerpts, IMAGE processing, GENERALIZATION

Artificial Intelligence in Diagnosis of Heart Failure.

Autoren: Yuji Xie Linyue Zhang Wei Sun et al.

Quelle: Journal of the American Heart Association; 4/15/2025, Vol. 14 Issue 8, p1-14, 14p