Výsledky vyhledávání - conditional variational autoencoder*

Optimal Dimensionality Reduction using Conditional Variational AutoEncoder

Autoři: Boussam, Sana Carbone, Mathieu Gérard, Benoît a další

Přispěvatelé: Boussam, Sana Carbone, Mathieu Gérard, Benoît a další

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

Témata: 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

Popis souboru: application/pdf

Přístupová URL adresa: 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

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

Autoři: Jiang, Junhao Zuo, Yi Li, Zhiyuan

Zdroj: Reliability Engineering and System Safety. 267

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

Přístupová URL adresa: https://research.chalmers.se/publication/549377

Conditional variational autoencoders for generated hub genes in human endogenous retrovirus and periodontal inflammasome

Autoři: Pradeep Kumar Yadalam

Zdroj: Gaceta Médica de Caracas. 133:709-714

Vessel Trajectory Prediction Based on GAN with Kolmogorov-Arnold Network and Conditional Variational Autoencoder

Autoři: Yasi Tian Hui Zhang Weimin Huang

Zdroj: 2025 IEEE 20th International Symposium on Antenna Technology and Applied Electromagnetics (ANTEM). :101-104

Enhanced Seismic Ground Motion Modeling With Conditional Variational Autoencoder

Autoři: Pavan Mohan Neelamraju Akshay Pratap Singh STG Raghukanth

Zdroj: Earthquake Engineering and Resilience. 4:178-201

Distributional Drift Adaptation With Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting

Autoři: Hui He Qi Zhang Kun Yi a další

Zdroj: IEEE Transactions on Neural Networks and Learning Systems. 36:7287-7301

Témata: FOS: Computer and information sciences, Computer Science - Machine Learning, I.2.6, 0202 electrical engineering, electronic engineering, information engineering, 68Txx, 02 engineering and technology, 01 natural sciences, 0105 earth and related environmental sciences, Machine Learning (cs.LG)

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/38683706
http://arxiv.org/abs/2209.00654

Feasibility of Conditional Variational Autoencoders for Phase-Averaged Synthetic Time Series

Autoři: Rüb, Matthias Grüber, Jens Schotten, Hans D

Zdroj: European Conference on Cyber Warfare and Security. 24:614-620

Estimation of the coverage area with ResNet-based Conditional Variational Autoencoder

Autoři: Uğur Erbaș Adem Avcı Mehmet Barış Tabakcıoğlu

Zdroj: 2025 33rd Signal Processing and Communications Applications Conference (SIU). :1-4

CSSinger: End-to-End Chunkwise Streaming Singing Voice Synthesis System Based on Conditional Variational Autoencoder

Autoři: Cui, Jianwei Gu, Yu Chen, Shihao a další

Zdroj: Proceedings of the AAAI Conference on Artificial Intelligence. 39:23704-23714

Témata: Audio and Speech Processing (eess.AS), FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Audio and Speech Processing

Přístupová URL adresa: http://arxiv.org/abs/2412.08918

Individualized Estimation of Baseline Retinal Nerve Fiber Layer Thickness Using Conditional Variational Autoencoder

Autoři: Ou Tan, PhD Keke Liu, MD Aiyin Chen, MD a další

Zdroj: Ophthalmology Science, Vol 5, Iss 6, Pp 100849- (2025)

Témata: Conditional variational autoencoder, Glaucoma, Nerve fiber layer thickness, OCT, Ophthalmology, RE1-994

Popis souboru: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2666914525001472; https://doaj.org/toc/2666-9145

Přístupová URL adresa: https://doaj.org/article/2533b76a4100467c80b14765360ef5b5

Generative Design of Thermoset Shape Memory Polymers Driven by Chemical Group: A Conditional Variational Autoencoder Approach

Autoři: Borun Das Andrew Peters Guoqiang Li a další

Zdroj: Journal of Polymer Science. 63:1334-1344

Speech Audio Generation from Dynamic MRI via a Knowledge Enhanced Conditional Variational Autoencoder

Autoři: Yaxuan Li Han Jiang Yifei Ma a další

Zdroj: Lecture Notes in Computer Science ISBN: 9783032049469

STO-CVAE: state transition-oriented conditional variational autoencoder for data augmentation in disability classification

Autoři: Seong Jin Bang Min Jung Kang Min-Goo Lee a další

Zdroj: Complex & Intelligent Systems, Vol 10, Iss 3, Pp 4201-4222 (2024)

Témata: Conditional variational autoencoder, Human pose estimation, Electronic computers. Computer science, 0202 electrical engineering, electronic engineering, information engineering, QA75.5-76.95, Information technology, 02 engineering and technology, Class imbalance problem, Human action recognition, T58.5-58.64

Přístupová URL adresa: https://doaj.org/article/c72756fe6c6b4986a006e7d63158d538

Enhancing Global Surface Soil Moisture Estimation From ESA CCI and SMAP Product With a Conditional Variational Autoencoder

Autoři: Changjiang Shi Zhijie Zhang Shengqing Xiong a další

Zdroj: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 9337-9359 (2024)

Témata: Conditional variational autoencoder (CVAE), SMAP L4, QC801-809, Geophysics. Cosmic physics, 0207 environmental engineering, 02 engineering and technology, 15. Life on land, surface soil moisture (SM), 01 natural sciences, Ocean engineering, product reconstruction, 13. Climate action, ESA climate change initiative (CCI), TC1501-1800, 0105 earth and related environmental sciences

Přístupová URL adresa: https://doaj.org/article/172f29a7106a48fb9baccd27a3d5514e

Anomaly detection in KOMAC high-power systems using transformer-based conditional variational autoencoder

Autoři: Gi-Hu Kim Hae-Seong Jeong Han-Sung Kim a další

Zdroj: Journal of the Korean Physical Society. 87:883-891

Enhancing NER by Harnessing Multiple Datasets with Conditional Variational Autoencoders

Autoři: Taku Oi Makoto Miwa

Zdroj: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). :1107-1117

Automated Anomaly Detection and Risk Stratification in Polymerization Reactor Systems via Multi-Modal Data Fusion and Conditional Variational Autoencoders

Autoři: Freederia AI Researcher

Synthetic Data Generation for Enhancing Text Classification Performance Using Conditional Variational Autoencoders

Autoři: Ömer Faruk Cebeci Mehmet Fatih Amasyali

Zdroj: Orclever Proceedings of Research and Development. 5:498-514

Accented Text-to-Speech Synthesis with a Conditional Variational Autoencoder

Autoři: Melechovsky, Jan Mehrish, Ambuj Sisman, Berrak a další

Zdroj: TENCON 2024 - 2024 IEEE Region 10 Conference (TENCON). :343-346

Témata: FOS: Computer and information sciences, Computer Science - Machine Learning, 03 medical and health sciences, Sound (cs.SD), Audio and Speech Processing (eess.AS), 0202 electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 0305 other medical science, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Machine Learning (cs.LG)

Přístupová URL adresa: http://arxiv.org/abs/2211.03316

Daily Power Scenario Generation Method for Multiple Wind Farms Based on Gaussian Mixture Clustering and Improved Conditional Variational Autoencoder

Autoři: Dan LI Yunyan LIANG Shuwei MIAO a další

Zdroj: Zhongguo dianli, Vol 57, Iss 12, Pp 17-29 (2024)

Témata: QC501-721, hyperspherical distribution, TK1001-1841, Production of electric energy or power. Powerplants. Central stations, Electricity, feature extraction, wind power scenario generation, conditional variational autoencoder, gaussian mixture model, regularization technique

Přístupová URL adresa: https://doaj.org/article/4465b91aecc5458fbe897fd9f023c8fb