Suchergebnisse - variational autoencoder

A supervised variational autoencoder framework for dimensionality reduction and predictive modeling in high-dimensional socioeconomic data

Autoren: Pei Xue Tianshun Li

Quelle: Journal of Economy and Technology, Vol 4, Iss, Pp 9-19 (2026)

Schlagwörter: High-dimensional data, Socioeconomic forecasting, Economics as a science, Electronic computers. Computer science, Supervised variational autoencoder, Social Sciences, QA75.5-76.95, Dimensionality reduction, HB71-74, Nonlinear representation learning, Predictive modeling

Zugangs-URL: https://doaj.org/article/38a6a8d09a8045ceaf6f58f757d3bf5e

Meta-learning approach for variational autoencoder hyperparameter tuning

Autoren: Michele Berti Matheus Camilo da Silva Sebastiano Saccani et al.

Weitere Verfasser: Michele Berti Matheus Camilo da Silva Sebastiano Saccani et al.

Quelle: Journal of Universal Computer Science, Vol 31, Iss 7, Pp 668-682 (2025)
JUCS-Journal of Universal Computer Science 31(7): 668-682

Schlagwörter: Meta-learning, VAE, Electronic computers. Computer science, Variational Autoencoders, HPO, QA75.5-76.95

Dateibeschreibung: text/html

Zugangs-URL: https://doaj.org/article/f646b0c98dba4398b1d9d571fb1acc61
https://hdl.handle.net/11368/3115691
https://hdl.handle.net/11368/3115691
https://doi.org/10.3897/jucs.124087

Optimal Dimensionality Reduction using Conditional Variational AutoEncoder

Autoren: Boussam, Sana Carbone, Mathieu Gérard, Benoît et al.

Weitere Verfasser: Boussam, Sana Carbone, Mathieu Gérard, Benoît et al.

Quelle: Transactions on Cryptographic Hardware and Embedded Systems, Vol 2025, Iss 3 (2025)

Schlagwörter: TK7885-7895, Computer engineering. Computer hardware, Deep Learning, Side-Channel Attacks, Variational AutoEncoder, Generative Models, Information technology, [INFO] Computer Science [cs], Optimal Dimensionality Reduction, T58.5-58.64, Optimal Attacks

Dateibeschreibung: application/pdf

Zugangs-URL: https://doaj.org/article/95892bfa2003433ea20557dbfd1de1dd
https://hal.science/hal-05148980v1
https://hal.science/hal-05148980v1/document
https://doi.org/10.46586/tches.v2025.i3.164-211

AI-driven music composition: Melody generation using Recurrent Neural Networks and Variational Autoencoders

Autoren: Hanbing Zhao Siran Min Jianwei Fang et al.

Quelle: Alexandria Engineering Journal, Vol 120, Iss, Pp 258-270 (2025)

Schlagwörter: Recurrent neural network, Emotional expression, Variational autoencoder, Diversity and coherence, TA1-2040, Melody generation, Engineering (General). Civil engineering (General), Automated music composition

Zugangs-URL: https://doaj.org/article/14c612ce44984ac798a108bbf5a1ab2c

Early Detection of Rotor Faults in Large Hydrogenerators Using Vibration Measurements, Variational Autoencoder, and Euclidean Distance

Autoren: Rony Ibrahim Ryad Zemouri Antoine Tahan et al.

Quelle: IEEE Transactions on Industry Applications. 61:9023-9032

Quantum Variational Autoencoder Based on Weak Measurements With Fuzzy Filtering of Input Data

Autoren: Vyacheslav Korolyov Maksim Ogurtsov Oleksandr Khodsinskyi

Quelle: Кібернетика та комп'ютерні технології, Iss 1, Pp 106-117 (2025)

Schlagwörter: neural network, weak measurements, Q300-390, variational autoencoder, fuzzy logic, Cybernetics, quantum computing

Zugangs-URL: https://doaj.org/article/e2bcdf8f56e649f69cdfca4e6ca97466

Using Variational Autoencoders for Out of Distribution Detection in Histological Multiple Instance Learning

Autoren: Francisco Javier Sáez-Maldonado Luz García Lee A. D. Cooper et al.

Quelle: Digibug. Repositorio Institucional de la Universidad de Granada
Universidad de Granada (UGR)
IEEE Access, Vol 13, Pp 133351-133369 (2025)

Schlagwörter: Variational Autoencoder, multiple instance learning, Multiple instance learning, variational autoencoder, Electrical engineering. Electronics. Nuclear engineering, Out-of-distribution detection, TK1-9971

Zugangs-URL: https://doaj.org/article/d59532d3f27648c7acac265ee56ae2d6
https://hdl.handle.net/10481/106026

Variational Autoencoder with Gaussian Random Field prior: Application to unsupervised animal detection in aerial images

Autoren: Gangloff, Hugo Pham, Minh-Tan Courtrai, Luc et al.

Weitere Verfasser: Gangloff, Hugo Pham, Minh-Tan Courtrai, Luc et al.

Quelle: ISPRS Journal of Photogrammetry and Remote Sensing. 218:600-609

Schlagwörter: [STAT.AP]Statistics [stat]/Applications [stat.AP], [STAT.AP] Statistics [stat]/Applications [stat.AP], [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Variational autoencoders anomaly detection Gaussian random fields aerial images, Gaussian random fields, aerial images, Variational autoencoders, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], anomaly detection, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing

Dateibeschreibung: application/pdf

Zugangs-URL: https://hal.science/hal-04719231v1/document
https://doi.org/10.1016/j.isprsjprs.2024.09.028
https://hal.science/hal-04719231v1
https://hal.science/hal-03774853v1
https://doi.org/10.1109/icip49359.2023.10222900
https://hal.science/hal-03774853v1/document

Contrastive Disentangled Variational Autoencoder for Collaborative Filtering

Autoren: Woo-Seong Yun Seong-Min Kang Yoon-Sik Cho

Quelle: IEEE Access, Vol 13, Pp 99221-99233 (2025)

Schlagwörter: contrastive disentanglement, collaborative filtering, variational autoencoder, Electrical engineering. Electronics. Nuclear engineering, Recommender system, TK1-9971

Zugangs-URL: https://doaj.org/article/288f75a7d5b644319ac26ca383ded865

Improving News Recommendation Accuracy Through Multimodal Variational Autoencoder and Adversarial Training

Autoren: Pei Tang Shuo Zhu Bilal Alatas

Quelle: IEEE Access, Vol 13, Pp 85269-85278 (2025)

Schlagwörter: multi-head attention mechanismmeta-learning, convolutional neural networks, deep learning, Variational autoencoder, Electrical engineering. Electronics. Nuclear engineering, generative adversarial networks, multimodal recommender systems, TK1-9971

Zugangs-URL: https://doaj.org/article/f4499787f0d249e18df95bcecd7a8942

Multi-modal graph convolutional network for vessel trajectory prediction based on cooperative intention enhance using conditional variational autoencoder

Autoren: Jiang, Junhao Zuo, Yi Li, Zhiyuan

Quelle: Reliability Engineering and System Safety. 267

Schlagwörter: Graph convolutional network, Trajectory prediction, Cooperative intention constructor, Intelligent situational awareness systems, Conditional variational autoencoder, Multi-modal interaction extractor

Zugangs-URL: https://research.chalmers.se/publication/549377

An Effective Pipeline for Training Variational Autoencoders for Synthesizable and Optimized Molecular Design

Autoren: Fardeen H. Mozumder Byung-Jun Yoon

Quelle: IEEE Access, Vol 13, Pp 529-540 (2025)

Schlagwörter: molecule optimization, variational autoencoder (VAE), Electrical engineering. Electronics. Nuclear engineering, synthesizability, Generative molecular design (GMD), TK1-9971

Zugangs-URL: https://doaj.org/article/284536da473f47c1b2cf05dc2180209c

Leveraging Vector Quantized Variational Autoencoder for Accurate Synthetic Data Generation in Multivariate Time Series

Autoren: Mohammad Diqi Ema Utami Kusrini Kusrini et al.

Quelle: MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer. 24:423-438

Fusion-aware quantum variational autoencoder for brain-heart signal modeling in mental health applications

Autoren: Ayesha Jabbar Huang Jianjun Muhammad Kashif Jabbar et al.

Quelle: Journal of King Saud University: Computer and Information Sciences, Vol 37, Iss 9, Pp 1-21 (2025)

Schlagwörter: Quantum computing, Variational autoencoder, Brainwave recognition, Mental health, Internet of things, Electronic computers. Computer science, QA75.5-76.95

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/1319-1578; https://doaj.org/toc/2213-1248

Zugangs-URL: https://doaj.org/article/f87a6c5e49ce4ca892e29cdce81c4624

Learning the Difference of Few-Shot Food Data Using Multivariate Knowledge-Guided Variational Autoencoder

Autoren: Yi Zhang Sheng Huang Mingjian Hong et al.

Quelle: IEEE Journal of Biomedical and Health Informatics. 29:5168-5180

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/40072861

RPI-GGCN: Prediction of RNA-Protein Interaction Based on Interpretability Gated Graph Convolution Neural Network and Co-Regularized Variational Autoencoders

Autoren: Yifei Wang Pengju Ding Congjing Wang et al.

Quelle: IEEE Transactions on Neural Networks and Learning Systems. 36:7681-7695

Schlagwörter: RNA, Humans, RNA-Binding Proteins, Computational Biology, Proteins, Neural Networks, Computer, Autoencoder, Algorithms

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/38709606

‏MCVAE: A New Multi-Conditional Approach for Scalable Emergency Ad Hoc Network Design and Node Localization Using Generative Variational Autoencoder

Autoren: M. A. A. Jabbar A. R. Abbas R. F. Ghani

Quelle: International Journal of Engineering. 39:1390-1401

AN UNSUPERVISED APPROACH FOR DETECTION OF ENCRYPTED IOT ANOMALIES USING VARIATIONAL AUTOENCODER AND ISOLATION FOREST TECHNIQUES

Autoren: Sukanya N Raja S

Quelle: ICTACT Journal on Communication Technology. 16:3677-3685

scPerb: Predict single-cell perturbation via style transfer-based variational autoencoder

Autoren: Zijia, Tang Minghao, Zhou Kai, Zhang et al.

Quelle: Journal of Advanced Research. 75:189-198

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/39486785

Optimizing Multi-dimensional Feature Fusion and Anomaly Detection of Ground Faults in Power Distribution Networks Using Quantum Variational Autoencoders

Autoren: Zhihai Yan Zaixin Yang Jiali Liu et al.

Quelle: RE&PQJ. 23:40-51