Suchergebnisse - "convolutional variational autoencoders"

On the Exploration of Convolutional Variational Autoencoders for Analog Integrated Circuit Post-Placement Performance Regression

Autoren: Carlos Almeida Marco Oliveira Ricardo Martins

Quelle: 2025 21st International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuits Design (SMACD). :1-4

Subject Representation Learning from EEG using Graph Convolutional Variational Autoencoders

Autoren: Mishra, Aditya Samin, Ahnaf Mozib Etemad, Ali et al.

Quelle: ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :1-5

Schlagwörter: Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Signal Processing, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2501.16626

Robust Spectral Anomaly Detection in EELS Spectral Images via 3D Convolutional Variational Autoencoders

Autoren: Seyfal Sultanov R. A. W. Ayyubi James P. Buban et al.

Quelle: Small. 21

Convolutional Variational Autoencoders and Resampling Techniques with Generative Adversarial Network for Enhancing Internet of Thing Security

Autoren: Huiyao Dong I. V. Kotenko

Quelle: Pattern Recognition and Image Analysis. 34:562-569

Investigation of Convolutional Variational AutoEncoders for facies calibration within Ensemble-Smoother Multiple Data Assimilation

Autoren: Emanuela Abbate Laura Dovera Davide Di Curzio et al.

Quelle: ECMOR 2024. :1-16

Investigation of convolutional variational autoencoders for facies calibration within ensemble-smoother multiple data assimilation.

Autoren: Abbate, Emanuela Dovera, Laura Di Curzio, Davide et al.

Quelle: Computational Geosciences. Oct2025, Vol. 29 Issue 5, p1-22. 22p.

Latent space interpretation and visualisation for understanding the decisions of convolutional variational autoencoders trained with EEG topographic maps

Autoren: Ahmed, Taufique Longo, Luca

Quelle: CEUR Workshop Proceedings. 3554:65-70

Zugangs-URL: https://researchprofiles.tudublin.ie/en/publications/91016b0f-fdac-4655-b476-9a8c2cfc9692

Convolutional Variational Autoencoders for Spectrogram Compression in Automatic Speech Recognition

Autoren: Iakovenko, Olga Bondarenko, Ivan

Index Begriffe: Computer Science - Sound, Computer Science - Computation and Language, Electrical Engineering and Systems Science - Audio and Speech Processing, text

URL: http://arxiv.org/abs/2410.02560

Convolutional variational autoencoders for secure lossy image compression in remote sensing

Autoren: Giuliano, Alessandro Gadsden, S. Andrew Hilal, Waleed et al.

Index Begriffe: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2404.03696

Synthetic Generation of Cardiac MR Images Combining Convolutional Variational Autoencoders and Style Transfer

Autoren: Jose Manuel, Jaen-Lorites Manuel, Perez-Pelegri Valero, Laparra et al.

Quelle: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). :2084-2087

Schlagwörter: Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Heart, Magnetic Resonance Imaging, Algorithms

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/36086174

Scalable Graph Convolutional Variational Autoencoders

Autoren: Unyi, Dániel Gyires-Tóth, Bálint

Quelle: 2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI). :467-472

Schlagwörter: QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány, 05 social sciences, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 0503 education

Dateibeschreibung: text

Zugangs-URL: https://dblp.uni-trier.de/db/conf/saci/saci2021.html#UnyiG21
https://ieeexplore.ieee.org/document/9465579/

Examining the Size of the Latent Space of Convolutional Variational Autoencoders Trained With Spectral Topographic Maps of EEG Frequency Bands

Autoren: Taufique Ahmed Luca Longo Technological University Dublin, Ireland, under Grant PB04433.

Weitere Verfasser: Taufique Ahmed Luca Longo Technological University Dublin, Ireland, under Grant PB04433.

Quelle: IEEE Access, Vol 10, Pp 107575-107586 (2022)
Articles

Schlagwörter: and neural networks, frequency bands, spectral topographic maps, deep learning, Electroencephalography, 02 engineering and technology, Electrical and Computer Engineering, TK1-9971, latent space, convolutional variational autoencoder, 0202 electrical engineering, electronic engineering, information engineering, Electrical engineering. Electronics. Nuclear engineering

Dateibeschreibung: application/pdf

Zugangs-URL: https://doaj.org/article/e3dc65d627ad45ee95e687f9ea24f0ee
https://researchprofiles.tudublin.ie/en/publications/7e2afb67-6b80-443c-8a7f-5bbc0c8b03d8
https://doi.org/10.1109/ACCESS.2022.3212777
https://arrow.tudublin.ie/context/scschcomart/article/1193/viewcontent/Examining_the_Size_of_the_Latent_Space_of.pdf

Image De-Noising Using Convolutional Variational Autoencoders

Autoren: M Sreeteish

Quelle: International Journal for Research in Applied Science and Engineering Technology. 10:4002-4009

Robust Spectral Anomaly Detection in EELS Spectral Images via Three Dimensional Convolutional Variational Autoencoders

Autoren: Sultanov, Seyfal Buban, James P Klie, Robert F

Schlagwörter: FOS: Computer and information sciences, Condensed Matter - Materials Science, Computer Science - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Materials Science (cond-mat.mtrl-sci), FOS: Physical sciences, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2412.16200

Convolutional Variational Autoencoders for Image Clustering

Autoren: Ioannis A. Nellas Sotiris K. Tasoulis Vassilis P. Plagianakos

Quelle: 2021 International Conference on Data Mining Workshops (ICDMW). :695-702

Evolving Deep Convolutional Variational Autoencoders for Image Classification

Autoren: Xiangru Chen Yanan Sun Mengjie Zhang et al.

Quelle: IEEE Transactions on Evolutionary Computation. 25:815-829

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

Viability of Convolutional Variational Autoencoders for Lifelong Class Incremental Similarity Learning

Autoren: Jiahao Huo Terence L. van Zyl

Quelle: Communications in Computer and Information Science ISBN: 9783031490019

Real-time unsupervised monitoring of earth pressure balance shield-induced sinkholes in mixed-face ground conditions via convolutional variational autoencoders

Autoren: Jorge Loy-Benitez Hyun-Koo Lee Myung Kyu Song et al.

Quelle: Tunnelling and Underground Space Technology. 152:105908

Ship Detection in SAR Images Using Convolutional Variational Autoencoders

Autoren: Nuno Ferreira Margarida Silveira

Quelle: IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. :2503-2506

Schlagwörter: 0301 basic medicine, 03 medical and health sciences, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://dblp.uni-trier.de/db/conf/igarss/igarss2020.html#FerreiraS20

LHC hadronic jet generation using convolutional variational autoencoders with normalizing flows

Autoren: Orzari, Breno Chernyavskaya, Nadezda Cobe, Raphael Duarte, Javier Fialho, Jefferson Gunopulos, Dimitrios Kansal, Raghav Pierini, Maurizio Tomei, Thiago Touranakou, Mary

Zugangs-URL: https://pergamos.lib.uoa.gr/uoa/dl/object/uoadl:3489618