Suchergebnisse - "Multi-modal autoencoder"

Robust anomaly detection through multi-modal autoencoder fusion for small vehicle damage detection

Autoren: Sara Khan Mehmed Yüksel Frank Kirchner

Quelle: Machine Learning with Applications, Vol 22, Iss , Pp 100794- (2025)

Schlagwörter: Multi-modal, Autoencoders, Sensors, Automotive, Signal processing, Deep learning, Cybernetics, Q300-390, Electronic computers. Computer science, QA75.5-76.95

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S266682702500177X; https://doaj.org/toc/2666-8270

Zugangs-URL: https://doaj.org/article/967adec2abd14f09aaf640bb3175d464

Robust Anomaly Detection through Multi-Modal Autoencoder Fusion for Small Vehicle Damage Detection

Autoren: Khan, Sara Yüksel, Mehmed Kirchner, Frank

Schlagwörter: Machine Learning, FOS: Computer and information sciences, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2509.01719

Prediction of Drug-Target Interactions Based on Multi-Modal Autoencoder

Autoren: Cheng Wang Zonghao Zhao Pandeng Gu et al.

Quelle: 2023 16th International Conference on Advanced Computer Theory and Engineering (ICACTE). :1-5

Prediction of Recurrence Probability of Thyroid Cancer Patients using Similarity Loss based Multi-modal Autoencoder

Autoren: Jaehyun Kim Hyunjung Shin

Quelle: 2023 IEEE International Conference on Big Data and Smart Computing (BigComp). :358-360

Thermal-visible stereo matching at night based on Multi-Modal Autoencoder

Autoren: Quan Zhang Yiran Li Le Yang et al.

Quelle: Infrared Physics & Technology. 136:105010

DOTA: Deep Learning Optimal Transport Approach to Advance Drug Repositioning for Alzheimer’s Disease

Autoren: Jacqueline Chyr Haoran Gong Xiaobo Zhou

Quelle: Biomolecules, Vol 12, Iss 196, p 196 (2022)

Schlagwörter: Alzheimer’s disease, drug repositioning, deep learning, multi-modal autoencoder, optimal transport problem, reactome, Microbiology, QR1-502

Relation: https://www.mdpi.com/2218-273X/12/2/196; https://doaj.org/toc/2218-273X; https://doaj.org/article/89afc13c2b2b497b9f35e28d48824c57

Verfügbarkeit: https://doi.org/10.3390/biom12020196
https://doaj.org/article/89afc13c2b2b497b9f35e28d48824c57

Learning Digital Geographies through a Graph-Based Semi-supervised Approach

Autoren: P Liu S De Sabbata

Schlagwörter: IR content, social media, multi-modal autoencoder, neural network, graph convolutional network, digital geographies

Relation: 2381/10230632.v1

Verfügbarkeit: https://figshare.com/articles/conference_contribution/Learning_Digital_Geographies_through_a_Graph-Based_Semi-supervised_Approach/10230632

Learning Digital Geographies through a Graph-Based Semi-supervised Approach. GeoComputation 2019

Autoren: Pengyuan Liu Stef De Sabbata

Schlagwörter: Geospatial information systems and geospatial data modelling, social media, multi-modal autoencoder, neural network, graph convolutional network, digital geographies, Geospatial Information Systems

Verfügbarkeit: https://doi.org/10.17608/k6.auckland.9848213.v2
https://figshare.com/articles/conference_contribution/Learning_Digital_Geographies_through_a_Graph-Based_Semi-supervised_Approach/9848213

A graph-based semi-supervised approach to classification learning in digital geographies

Autoren: Pengyuan Liu Stefano De Sabbata

Schlagwörter: Social media, Multi-modal autoencoder, Neural network, Graph convolutional network, Digital geographies

Relation: 2381/13522676.v1

Verfügbarkeit: https://figshare.com/articles/journal_contribution/A_graph-based_semi-supervised_approach_to_classification_learning_in_digital_geographies/13522676

MM-LDM: Multi-Modal Latent Diffusion Model for Sounding Video Generation

Autoren: Sun, Mingzhen Wang, Weining Qiao, Yanyuan et al.

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

URL: http://arxiv.org/abs/2410.01594

Generic Multi-modal Representation Learning for Network Traffic Analysis

Autoren: Gioacchini, Luca Drago, Idilio Mellia, Marco et al.

Index Begriffe: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, text

URL: http://arxiv.org/abs/2405.02649

Location and amount of joint involvement differentiates rheumatoid arthritis into different clinical subsets.

Autoren: Maarseveen, Tjardo D. Maurits, Marc P. Coletto, Lavinia Agra et al.

Quelle: NPJ Digital Medicine; 10/23/2025, Vol. 8 Issue 1, p1-11, 11p

Schlagwörter: RHEUMATOID arthritis risk factors, RHEUMATOID arthritis treatment, RISK assessment, ACADEMIC medical centers, CLUSTER analysis (Statistics), DATA analysis, RHEUMATOID arthritis, SYNOVIAL fluid, KRUSKAL-Wallis Test, DESCRIPTIVE statistics, JOINTS (Anatomy), KAPLAN-Meier estimator, STATISTICS, ANALYSIS of variance, SURVIVAL analysis (Biometry), DATA analysis software, BIOMARKERS, PROPORTIONAL hazards models, SYMPTOMS

Geografische Kategorien: NETHERLANDS

Modality Information Aggregation Graph Attention Network with Adversarial Training for Multi-Modal Knowledge Graph Completion.

Autoren: Yilahun, Hankiz Aili, Elyar Imam, Seyyare et al.

Quelle: Information; Oct2025, Vol. 16 Issue 10, p907, 14p

Schlagwörter: KNOWLEDGE graphs, GRAPH neural networks, DATA integration, MACHINE learning, FEATURE extraction, SEMANTICS

Enhancing Vulnerability Prioritization in Cloud Computing Using Multi-View Representation Learning.

Autoren: Ullman, Steven Samtani, Sagar Zhu, Hongyi et al.

Quelle: Journal of Management Information Systems; 2024, Vol. 41 Issue 3, p708-743, 36p

Schlagwörter: VIRTUAL machine systems, WEB services, CLOUD computing, BEHAVIORAL research, SECURITY personnel, DEEP learning

Contrastive Multi-Modal Representation Learning for Spark Plug Fault Diagnosis

Autoren: Modarres, Ardavan Eivaghi, Vahid Mohammad-Zadeh Shoorehdeli, Mahdi Aliyari et al.

Index Begriffe: Computer Science - Machine Learning, Electrical Engineering and Systems Science - Systems and Control, text

URL: http://arxiv.org/abs/2311.02282

A graph-based semi-supervised approach to classification learning in digital geographies.

Autoren: Liu, Pengyuan De Sabbata, Stefano

Quelle: Computers, Environment & Urban Systems. Mar2021, Vol. 86, pN.PAG-N.PAG. 1p.

Schlagwörter: *CONVOLUTIONAL neural networks, *FOLKSONOMIES, *DEEP learning, *HUMAN geography, *USER-generated content, *URBAN geography, *VIDEO coding

Comparative benchmarking of single-cell clustering algorithms for transcriptomic and proteomic data.

Autoren: Yin, Yu-Hang Wang, Fang Li, Wei et al.

Quelle: Genome Biology; 9/3/2025, Vol. 26 Issue 1, p1-34, 34p

Small Language Models for Speech Emotion Recognition in Text and Audio Modalities.

Autoren: Gómez-Sirvent, José L. López de la Rosa, Francisco Sánchez-Reolid, Daniel et al.

Quelle: Applied Sciences (2076-3417); Jul2025, Vol. 15 Issue 14, p7730, 21p

Schlagwörter: EMOTION recognition, LANGUAGE models, ENSEMBLE learning, CONTEXTUAL analysis

Multi Task Learning based Framework for Multimodal Classification

Autoren: Danting Zeng

Quelle: Proceedings of the Third Workshop on Multimodal Artificial Intelligence. :30-35

Schlagwörter: 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://www.aclweb.org/anthology/2021.maiworkshop-1.5/

PTFNet: Robotic-Relevant, Single-View Obstacle Footprint Estimation From Sparse and Incomplete Point Clouds

Autoren: Konrad P. Cop Tomasz P. Trzciński

Quelle: IEEE Access, Vol 13, Pp 72812-72828 (2025)

Schlagwörter: obstacle reconstruction, 3D perception, robot navigation, occupancy estimation, Electrical engineering. Electronics. Nuclear engineering, Deep learning for visual perception, TK1-9971

Zugangs-URL: https://doaj.org/article/4f4b99d2421645b788c59b859177a25f