Výsledky vyhľadávania - sparse convolutional autoencoder ((space OR sage))

Leveraging variant of CAE with sparse convolutional embedding and two-stage application-driven data augmentation for image clustering.

Autori: Liu, Yanming Liu, Jinglei

Zdroj: Soft Computing - A Fusion of Foundations, Methodologies & Applications. Feb2025, Vol. 29 Issue 3, p1419-1435. 17p.

Predmety: *DATA augmentation, *AUTOENCODERS

Deep-Learning-Based Dictionary Construction for MIMO Radar Detection in Complex Scenes

Autori: Hongyan Wang He Zhou Qinghua Guo a ďalší

Zdroj: IEEE Internet of Things Journal. 11:12419-12434

Predmety: nonlinear correction, Multiple-input multiple-output (MIMO) radar, transceiving space-time resource allocation, sparse representation, complex-valued convolutional autoencoder

Reconstructing flow field from sparse sensor data: A deep learning framework combining autoencoder and cross-attention mechanism.

Autori: Kong, Xuan Fan, Qipei Li, Jinzhao a ďalší

Zdroj: Physics of Fluids. Sep2025, Vol. 37 Issue 9, p1-12. 12p.

Predmety: *AUTOENCODERS, *MACHINE learning, *AERODYNAMIC measurements, *ATMOSPHERIC models, *DETECTORS

A novel multichannel sparse convolutional autoencoder for electrocardiogram signal compression.

Autori: Bekiryazıcı, Tahir Damkacı, Mehmet Aydemir, Gürkan a ďalší

Zdroj: Journal of Electrocardiology. Nov2025, Vol. 93, pN.PAG-N.PAG. 1p.

DOA estimation method for sparse arrays based on deep convolutional autoencoder and deep convolutional neural network.

Autori: Guo, Shuhan Zhang, Qin Fu, Xiaolong a ďalší

Zdroj: Digital Signal Processing. Jan2026:Part D, Vol. 168, pN.PAG-N.PAG. 1p.

Predmety: *DIRECTION of arrival estimation, *CONVOLUTIONAL neural networks, *SPARSE matrices, *MULTIPLE Signal Classification, *FEATURE extraction, *COVARIANCE matrices, *ESTIMATION theory

3D Segmentation of Perivascular Spaces on T1-Weighted 3 Tesla MR Images With a Convolutional Autoencoder and a U-Shaped Neural Network

Autori: Philippe Boutinaud Ami Tsuchida Alexandre Laurent a ďalší

Zdroj: Frontiers in Neuroinformatics, Vol 15 (2021)

Predmety: perivascular space, deep learning, U-net, MRI, brain cohort, segmentation, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571

Popis súboru: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/fninf.2021.641600/full; https://doaj.org/toc/1662-5196

Prístupová URL adresa: https://doaj.org/article/ed3360c8420941b78108600cebb4efc3

A novel intelligent model for high-precision reconstruction of dam-break free surface profiles from sparse monitoring data.

Autori: Deng, Yangyu Liu, Yakun Zhang, Di a ďalší

Zdroj: Physics of Fluids. Feb2025, Vol. 37 Issue 2, p1-28. 28p.

Predmety: *PROPER orthogonal decomposition, *WATER levels, *AUTOENCODERS, *FREE surfaces, *FLOOD control

Real-time forecasting of chaotic dynamics from sparse data and autoencoders

Autori: Özalp, Elise Nóvoa, Andrea Magri, Luca

Predmety: 37N10, 65P20, Chaotic Dynamics, FOS: Physical sciences, Chaotic Dynamics (nlin.CD)

Prístupová URL adresa: http://arxiv.org/abs/2508.08729

Application of a Convolutional Autoencoder to Half Space Radar Hrrp Recognition

Autori: Yong-Jun Xie Shi-Hao Yu

Zdroj: 2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). :48-53

Predmety: 2. Zero hunger, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 3. Good health

Prístupová URL adresa: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8521306
https://ieeexplore.ieee.org/document/8521306/

Temperature scaling unmixing framework based on convolutional autoencoder

Autori: Jin Xu Mingming Xu Shanwei Liu a ďalší

Zdroj: International Journal of Applied Earth Observations and Geoinformation, Vol 129, Iss , Pp 103864- (2024)

Predmety: Hyperspectral unmixing, Convolutional autoencoder, Sparsity constraint, Temperature scaling, Equal-frequency binning, Physical geography, GB3-5030, Environmental sciences, GE1-350

Popis súboru: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S1569843224002188; https://doaj.org/toc/1569-8432

Prístupová URL adresa: https://doaj.org/article/aa7533b7fbf447e1a8b51942e4c8c5f4

3D segmentation of perivascular spaces on T1-weighted 3 Tesla MR images with a convolutional autoencoder and a U-shaped neural network

Autori: Boutinaud, P. Tsuchida, A. Laurent, A. a ďalší

Zdroj: Frontiers in Neuroinformatics

Popis súboru: application/pdf

Prístupová URL adresa: http://hdl.handle.net/21.11116/0000-000B-12F0-1
http://hdl.handle.net/21.11116/0000-000B-12EE-5

Application of Artificial Intelligence Technologies as an Intervention for Promoting Healthy Eating and Nutrition in Older Adults: A Systematic Literature Review.

Autori: Kalu, Kingsley Ataguba, Grace Onifade, Oyepeju a ďalší

Zdroj: Nutrients. Oct2025, Vol. 17 Issue 20, p3223. 33p.

Unsupervised learning with sparse space-and-time autoencoders

Autori: Graham, Benjamin

Témy: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, text

URL: http://arxiv.org/abs/1811.10355

3D segmentation of perivascular spaces on T1-weighted 3 Tesla MR images with a convolutional autoencoder and a U-shaped neural network

Autori: Philippe Boutinaud Ami Tsuchida Alexandre Laurent a ďalší

Zdroj: Front Neuroinform
Frontiers in Neuroinformatics, Vol 15 (2021)

Predmety: 03 medical and health sciences, 0302 clinical medicine, segmentation, deep learning, Neurosciences. Biological psychiatry. Neuropsychiatry, perivascular space, U-net, brain cohort, MRI, RC321-571, Neuroscience

Prístupová URL adresa: https://oskar-bordeaux.fr/bitstream/20.500.12278/110227/1/BPH_FN_2021_Boutinaud.pdf
https://www.frontiersin.org/articles/10.3389/fninf.2021.641600/pdf
https://pubmed.ncbi.nlm.nih.gov/34262443
https://doaj.org/article/ed3360c8420941b78108600cebb4efc3
https://biorxiv.org/content/10.1101/2020.11.25.397364v1.full.pdf
https://www.biorxiv.org/content/10.1101/2020.11.25.397364v1

Automated Video-EEG Analysis in Epilepsy Studies: A Narrative Review of Advances and Challenges.

Autori: Zuev VA Salmagambetova EG Djakov SN a ďalší

Zdroj: Journal of medical systems [J Med Syst] 2025 Oct 20; Vol. 49 (1), pp. 142. Date of Electronic Publication: 2025 Oct 20.

Spôsob vydávania: Journal Article; Review

Informácie o časopise: Publisher: Kluwer Academic/Plenum Publishers Country of Publication: United States NLM ID: 7806056 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-689X (Electronic) Linking ISSN: 01485598 NLM ISO Abbreviation: J Med Syst Subsets: MEDLINE

Výrazy zo slovníka MeSH: Electroencephalography*/methods , Epilepsy*/diagnosis , Epilepsy*/physiopathology , Video Recording*/methods, Humans ; Machine Learning

Sparse Representations for Object- and Ego-Motion Estimations in Dynamic Scenes

Autori: Hirak J. Kashyap Charless C. Fowlkes Jeffrey L. Krichmar

Zdroj: IEEE Transactions on Neural Networks and Learning Systems, vol 32, iss 6

Predmety: FOS: Computer and information sciences, Artificial intelligence, Computer Science - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), cs.LG, Computer Science - Computer Vision and Pattern Recognition, ego motion, 02 engineering and technology, Computer Vision and Multimedia Computation, Optical imaging, Machine Learning (cs.LG), Information and Computing Sciences, 0202 electrical engineering, electronic engineering, information engineering, Training, Artificial Intelligence & Image Processing, object motion, sparse representation, cs.CV, overcomplete basis, Cameras, 16. Peace & justice, Dynamics, Convolutional autoencoder, Motion segmentation, Estimation, Adaptive optics

Popis súboru: application/pdf

Prístupová URL adresa: https://escholarship.org/content/qt7p53p8zv/qt7p53p8zv.pdf?t=qgb7mw
https://pubmed.ncbi.nlm.nih.gov/32687472
http://arxiv.org/abs/1903.03731
https://europepmc.org/article/MED/32687472
https://arxiv.org/pdf/1903.03731.pdf
https://arxiv.org/abs/1903.03731
https://par.nsf.gov/biblio/10191836-sparse-representations-object-ego-motion-estimations-dynamic-scenes
https://pubmed.ncbi.nlm.nih.gov/32687472/
https://ui.adsabs.harvard.edu/abs/2019arXiv190303731K/abstract
https://escholarship.org/uc/item/7p53p8zv

Autoencoders for Semi-Supervised Water Level Modeling in Sewer Pipes with Sparse Labeled Data

Autori: Rius, Ferran Plana Philipsen, Mark P. Tur, Josep Maria Mirats a ďalší

Zdroj: Rius, F P, Philipsen, M P, Tur, J M M, Moeslund, T B, Bahón, C A & Casas, M 2022, 'Autoencoders for Semi-Supervised Water Level Modeling in Sewer Pipes with Sparse Labeled Data', Water (Switzerland), vol. 14, no. 3, 333. https://doi.org/10.3390/w14030333

Predmety: Autoencoders and latent space, Data distribution, Self-supervised, Semi-supervised, Sparse data, Supervised, Water

Popis súboru: application/pdf

Relation: info:eu-repo/semantics/altIdentifier/pissn/2073-4441

Dostupnosť: https://vbn.aau.dk/da/publications/65dbf42a-774e-45d2-906a-47e7bd52a17d
https://doi.org/10.3390/w14030333
https://vbn.aau.dk/ws/files/482740247/water_14_00333.pdf
https://www.scopus.com/pages/publications/85123710424

Convolutional autoencoder-based color image classification using chroma subsampling in YCbCr space

Autori: Zuhe Li Fengqin Wang Yangyu Fan

Zdroj: 2015 8th International Congress on Image and Signal Processing (CISP). :351-355

Predmety: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 10. No inequality

Prístupová URL adresa: https://ieeexplore.ieee.org/document/7407903/

Deep Learning Augmented Data Assimilation: Reconstructing Missing Information with Convolutional Autoencoders

Autori: Wang, Yueya Shi, Xiaoming Lei, Lili a ďalší

Zdroj: Monthly Weather Review. 150:1977-1991

Predmety: 13. Climate action, Data assimilation, 01 natural sciences, 0105 earth and related environmental sciences

Applying Convolutional Neural Networks to data on unstructured meshes with space-filling curves.

Autori: Heaney, Claire E. Li, Yuling Matar, Omar K. a ďalší

Zdroj: Neural Networks. Jul2024, Vol. 175, pN.PAG-N.PAG. 1p.

Predmety: *CONVOLUTIONAL neural networks, *IMAGE recognition (Computer vision), *TRAVELING salesman problem, *RECURRENT neural networks, *IMAGE compression, *PARTIAL differential equations, *FINITE element method