Suchergebnisse - "Convolutional variational autoencoder"
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Autoren: et al.
Quelle: Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Schlagwörter: Artificial intelligence, Clustering algorithm, Convolutional variational autoencoder (CVAE), Deep learning, UNet, Medicine, Science
Dateibeschreibung: electronic resource
Relation: https://doaj.org/toc/2045-2322
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2
Autoren: et al.
Weitere Verfasser: et al.
Quelle: Machine Learning and Knowledge Extraction, Vol 5, Iss 4, Pp 1493-1518 (2023)
Schlagwörter: TK7885-7895, convolutional variational autoencoder (CVAE), Computer engineering. Computer hardware, mm-wave radar sensor, 213 Electronic, automation and communications engineering, electronics, human activity recognition (HAR), deep neural networks (DNNs), dynamic time warping (DTW)
Dateibeschreibung: fulltext
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3
Autoren: et al.
Weitere Verfasser: et al.
Quelle: Articles
Schlagwörter: Electrical and Electronics, spectral topographic maps, 0211 other engineering and technologies, deep learning, Electroencephalography, 02 engineering and technology, latent space interpretation, 7. Clean energy, 12. Responsible consumption, convolutional variational autoencoder, 13. Climate action, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering
Dateibeschreibung: application/pdf
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4
Autoren: et al.
Quelle: Journal of King Saud University: Computer and Information Sciences, Vol 34, Iss 6, Pp 3332-3342 (2022)
Schlagwörter: 2. Zero hunger, Electronic computers. Computer science, Feature learning, 0202 electrical engineering, electronic engineering, information engineering, Deep learning, QA75.5-76.95, 02 engineering and technology, 15. Life on land, 01 natural sciences, Convolutional variational autoencoder, Tea clones recognition, 0105 earth and related environmental sciences
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5
Autoren: et al.
Quelle: Energy Science & Engineering, Vol 10, Iss 6, Pp 1855-1873 (2022)
Schlagwörter: convolutional variational autoencoder, Technology, 13. Climate action, Science, generative model, wind power, hub‐height wind, 01 natural sciences, 7. Clean energy, probabilistic forecasting, 0105 earth and related environmental sciences
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Autoren: et al.
Quelle: IEEE Access, Vol 10, Pp 57835-57849 (2022)
Schlagwörter: convolutional variational autoencoder, multivariate time series, 0202 electrical engineering, electronic engineering, information engineering, Anomaly detection, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, threshold setting strategy, TK1-9971
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Autoren:
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Quelle: IEEE Access, Vol 10, Pp 107575-107586 (2022)
ArticlesSchlagwörter: 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 -
8
Autoren: et al.
Quelle: IEEE Access, Vol 9, Pp 52352-52363 (2021)
Schlagwörter: convolutional variational autoencoder, 0209 industrial biotechnology, neural network, 0202 electrical engineering, electronic engineering, information engineering, deep learning, imbalanced data, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, Classification, unsupervised pre-training, TK1-9971
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9
Autoren: et al.
Weitere Verfasser: et al.
Quelle: IEEE Access, Vol 8, Pp 5438-5454 (2020)
Schlagwörter: Hydrogenerators, diagnosis, feature extraction, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], 15. Life on land, 7. Clean energy, TK1-9971, convolutional variational autoencoder, model interpretation, deep neural networks, 13. Climate action, data visualization, generative model, Electrical engineering. Electronics. Nuclear engineering, partial discharges
Dateibeschreibung: application/pdf
Zugangs-URL: https://ieeexplore.ieee.org/ielx7/6287639/8948470/08944065.pdf
https://doaj.org/article/e4b6f42fa99746b78b183493842fbf33
https://hal.archives-ouvertes.fr/hal-02462252/document
https://doi.org/10.1109/ACCESS.2019.2962775
https://dblp.uni-trier.de/db/journals/access/access8.html#ZemouriLAHKT20
https://ieeexplore.ieee.org/document/8944065/
https://hal.archives-ouvertes.fr/hal-02462252
https://hal.science/hal-02462252v1/document
https://hal.science/hal-02462252v1
https://doi.org/10.1109/access.2019.2962775 -
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Autoren:
Weitere Verfasser:
Quelle: Articles
Schlagwörter: Electroencephalography, convolutional variational autoencoder, latent space interpretation, deep learning, spectral topographic maps, Computer Engineering
Dateibeschreibung: application/pdf
Relation: https://arrow.tudublin.ie/scschcomart/219; https://arrow.tudublin.ie/context/scschcomart/article/1232/viewcontent/Interpreting_Disentangled_Representations_of_Person_Specific_Convolutional_Variational_Autoencoders_of_Spatially_Preserving_EEG_Topographic_Maps_via_Clustering_and_Visual_Plausibility.pdf
Verfügbarkeit: https://arrow.tudublin.ie/scschcomart/219
https://doi.org/10.3390/info14090489
https://arrow.tudublin.ie/context/scschcomart/article/1232/viewcontent/Interpreting_Disentangled_Representations_of_Person_Specific_Convolutional_Variational_Autoencoders_of_Spatially_Preserving_EEG_Topographic_Maps_via_Clustering_and_Visual_Plausibility.pdf -
11
Autoren: Yokkampon, Umaporn
Schlagwörter: Anomaly detection, Multivariate time series, Convolutional variational autoencoder, Data mining, Threshold setting strategy
Dateibeschreibung: application/pdf
Degree: 博士（情報工学） -- 九州工業大学
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12
Autoren: et al.
Quelle: Frontiers in Robotics and AI, Vol 9 (2022)
Schlagwörter: convolutional variational autoencoder, Gaussian process, hidden semi-Markov model, spatio-temporal categorization, segmentation, unsupervised learning, Mechanical engineering and machinery, TJ1-1570, Electronic computers. Computer science, QA75.5-76.95
Dateibeschreibung: electronic resource
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13
Autoren: et al.
Quelle: Energies, Vol 14, Iss 5232, p 5232 (2021)
Schlagwörter: NGSIM, occupancy grid, convolutional variational autoencoder, Technology
Relation: https://www.mdpi.com/1996-1073/14/17/5232; https://doaj.org/toc/1996-1073; https://doaj.org/article/d64e795938f14ccd9170411d6cbbdb3b
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Autoren: et al.
Quelle: Proceedings of the Annual Conference of JSAI. 2022, :3
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Autoren:
Weitere Verfasser:
Quelle: UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)Schlagwörter: Artificial intelligence, Text-to-speech software, Deep learning (Machine learning), Intel·ligència artificial, Intel·ligència Artificial Generativa, Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial, Autoencoders, Music Representation, Convolutional Autoencoder (CAE), Síntesi d'àudio, Representació musical, Drum Sample Synthesis, Convolutional Variational Autoencoder (CVAE), Algorithmic Composition, Generative Artificial Intelligence, Deep Learning, Producció musical amb IA, AI-Driven Music Production, Composició algorítmica, Síntesi de la parla (Programari), Aprenentatge profund
Dateibeschreibung: application/pdf
Zugangs-URL: https://hdl.handle.net/2117/420327
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Autoren: et al.
Quelle: Proceedings of the Annual Conference of JSAI. 2021, :2
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Autoren: et al.
Quelle: Proceedings of the Annual Conference of JSAI. 2020, :1
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18
Autoren: et al.
Weitere Verfasser: et al.
Schlagwörter: 히스토리매칭, 채널저류층, 3D 저류층, 앙상블 기반 방법, 기계 학습, beta-convolutional variational autoencoder, 622.33
Dateibeschreibung: xiii, 158
Relation: 000000178440; https://hdl.handle.net/10371/196356; https://dcollection.snu.ac.kr/common/orgView/000000178440; 000000000050▲000000000058▲000000178440▲
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Autoren: et al.
Quelle: International Conference on Medical Image Computing and Computer-Assisted Intervention. :249-256
Schlagwörter: Lung nodule segmentation, Anomaly detection, Convolutional variational autoencoder, Medical Technology, Medicinsk teknologi
Dateibeschreibung: print
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