Výsledky vyhledávání - compressed autoencoder

CAFNet: Compressed Autoencoder-based Federated Network for Anomaly Detection

Autoři: Abu Saleh Md Tayeen Satyajayant Misra Huiping Cao a další

Zdroj: MILCOM 2023 - 2023 IEEE Military Communications Conference (MILCOM). :325-330

Object Detection in Compressed LIDAR 3D Point Clouds

Deteção de Objetos em Nuvens de Pontos LiDAR 3D Comprimidas

Autoři: Filipe, Bernardo Paisana da Silva

Témata: Point Clouds, Semantic Segmentation, Latent-Domain Inference, Variational Autoencoder, Range Image Compression, Nuvens de Pontos, Segmentação Semântica, Inferência no Domínio Latente, Autoencoder Variacional, Compressão de Imagens de Profundidade

Dostupnost: https://hdl.handle.net/10316/120395

Deriving disease modules from the compressed transcriptional space embedded in a deep autoencoder

Autoři: Dwivedi, Sanjiv Tjärnberg, Andreas Tegner, Jesper a další

Zdroj: Nature Communications. 11(1)

Popis souboru: electronic

Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-168846
https://liu.diva-portal.org/smash/get/diva2:1466243/FULLTEXT01.pdf

Motor PHM on Edge Computing with Anomaly Detection and Fault Severity Estimation through Compressed Data Using PCA and Autoencoder

Autoři: Jong Hyun Choi Sung Kyu Jang Woon Hyung Cho a další

Zdroj: Machine Learning and Knowledge Extraction, Vol 6, Iss 3, Pp 1466-1483 (2024)

Témata: motor PHM, edge computing, anomaly detection, fault severity estimation, data compression, PCA, Computer engineering. Computer hardware, TK7885-7895

Popis souboru: electronic resource

Relation: https://www.mdpi.com/2504-4990/6/3/69; https://doaj.org/toc/2504-4990

Přístupová URL adresa: https://doaj.org/article/9e40609bb55b48d09f3dda896405cceb

Upsampling of compressed financial time-series data using a jointly trained Vector Quantized Variational Autoencoder neural network

Orthogonal Features Based EEG Signals Denoising Using Fractional and Compressed One-Dimensional CNN Autoencoder

Autoři: Subham Nagar Ahlad Kumar

Zdroj: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 30, Pp 2474-2485 (2022)

Témata: EEG signal denoising, convolutional neural networks, autoencoder, Tchebichef moments, compression, Medical technology, R855-855.5, Therapeutics. Pharmacology, RM1-950

Popis souboru: electronic resource

Relation: https://ieeexplore.ieee.org/document/9865981/; https://doaj.org/toc/1558-0210

Přístupová URL adresa: https://doaj.org/article/9da764368937466e802777fcbb537abb

Cross-Modal Contrastive Masked AutoEncoder for Compressed Video Pre-Training.

Autoři: Li B Chen J Li G a další

Zdroj: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society [IEEE Trans Image Process] 2025; Vol. 34, pp. 4500-4514.

Způsob vydávání: Journal Article

Informace o časopise: Publisher: Institute of Electrical and Electronics Engineers Country of Publication: United States NLM ID: 9886191 Publication Model: Print Cited Medium: Internet ISSN: 1941-0042 (Electronic) Linking ISSN: 10577149 NLM ISO Abbreviation: IEEE Trans Image Process Subsets: PubMed not MEDLINE; MEDLINE

The optimally designed autoencoder network for compressed sensing

Autoři: Zufan Zhang Yunfeng Wu Chenquan Gan a další

Zdroj: EURASIP Journal on Image and Video Processing, Vol 2019, Iss 1, Pp 1-12 (2019)

Témata: Compressed sensing, Stacked sparse denoising autoencoder, Deep learning, Multiple nonlinear measurement, Signal reconstruction, Electronics, TK7800-8360

Popis souboru: electronic resource

Relation: http://link.springer.com/article/10.1186/s13640-019-0460-5; https://doaj.org/toc/1687-5281

Přístupová URL adresa: https://doaj.org/article/8a403d77e24e4e6189172e0d05faf8f3

A compressed large language model embedding dataset of ICD 10 CM descriptions.

Autoři: Kane, Michael King, Casey Esserman, Denise a další

Zdroj: BMC Bioinformatics. 24(1)

Témata: Autoencoder, EHR, Electronic health records, ICD-10-CM, Large language model, NLP, International Classification of Diseases, Electronic Health Records, Language, Machine Learning, Natural Language Processing

Popis souboru: application/pdf

Přístupová URL adresa: https://escholarship.org/uc/item/4z7802gj
https://escholarship.org/content/qt4z7802gj/qt4z7802gj.pdf

Orthogonal Features-based EEG Signal Denoising using Fractionally Compressed AutoEncoder

Autoři: Nagar, Subham Kumar, Ahlad Swamy, M. N. S.

Témata: Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2102.08083

Orthogonal Features Based EEG Signals Denoising Using Fractional and Compressed One-Dimensional CNN AutoEncoder

Autoři: Nagar, Subham Kumar, Ahlad

Témata: Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, text

URL: http://arxiv.org/abs/2104.08120

Communication Optimization in Large Scale Federated Learning using Autoencoder Compressed Weight Updates

Autoři: Chandar, Srikanth Chandran, Pravin Bhat, Raghavendra a další

Témata: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Distributed, Parallel, and Cluster Computing, text

URL: http://arxiv.org/abs/2108.05670

Variational Autoencoders for Network Lifetime Enhancement in Wireless Sensors.

Autoři: Sengodan, Boopathi Chettiagounder Stanislaus, Prince Mary Arumugam, Sivakumar Sabapathy a další

Zdroj: Sensors (14248220); Sep2024, Vol. 24 Issue 17, p5630, 19p

Témata: DATA compression, DATA transmission systems, DISTRIBUTED sensors, COMPRESSED sensing, ENERGY consumption, WIRELESS sensor networks, DEEP learning, AUTOENCODERS

DC Disturbance Classification Method Based on Compressed Sensing and Encoder.

Autoři: Yu, Huanan Zhang, Xiang Wang, Jian

Zdroj: Energy Engineering; 2025, Vol. 122 Issue 12, p5055-5071, 17p

Témata: COMPRESSED sensing, POWER quality disturbances, DIMENSIONAL reduction algorithms, SIGNAL reconstruction, SIGNAL processing, HYBRID power, FEATURE extraction

Compressed unsupervised quantum state preparation with quantum autoencoders

A radar emitter structural identification method for complex conditions based on compressed sensing and an autoencoder.

Autoři: Song, Yu Zhang, Wei Yang, Yaozu a další

Zdroj: Nonlinear Dynamics; Aug2025, Vol. 113 Issue 15, p19159-19178, 20p

The Optimally Designed Deep Autoencoder-Based Compressive Sensing Framework for 1D and 2D Signals

Autoři: Irfan Ahmed Lunchakorn Wuttisittikulkij Aftab Khan a další

Zdroj: IEEE Access, Vol 12, Pp 150520-150539 (2024)

Témata: Compressed sensing, compressive sampling, reconstruction algorithms, sensing matrix, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Popis souboru: electronic resource

Relation: https://ieeexplore.ieee.org/document/10703043/; https://doaj.org/toc/2169-3536

Přístupová URL adresa: https://doaj.org/article/ae988f81f59746c18e5dbb0f480a6508

Masked autoencoder for highly compressed single-pixel imaging.

Autoři: Liu H Chang X Yan J a další

Zdroj: Optics letters [Opt Lett] 2023 Aug 15; Vol. 48 (16), pp. 4392-4395.

Způsob vydávání: Journal Article

Informace o časopise: Publisher: Optica Publishing Group Country of Publication: United States NLM ID: 7708433 Publication Model: Print Cited Medium: Internet ISSN: 1539-4794 (Electronic) Linking ISSN: 01469592 NLM ISO Abbreviation: Opt Lett Subsets: PubMed not MEDLINE; MEDLINE

Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization

Autoři: Grover, Aditya Ermon, Stefano

Témata: Statistics - Machine Learning, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, text

URL: http://arxiv.org/abs/1812.10539

Orthogonal features-based EEG signal denoising using fractionally compressed autoencoder

Autoři: Mallappa Kumara Swamy Subham Nagar Ahlad Kumar

Zdroj: Signal Processing. 188:108225

Témata: FOS: Computer and information sciences, Computer Science - Machine Learning, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Machine Learning (cs.LG)

Přístupová URL adresa: http://arxiv.org/pdf/2102.08083
http://arxiv.org/abs/2102.08083
https://dl.acm.org/doi/10.1016/j.sigpro.2021.108225
http://www.sciencedirect.com/science/article/pii/S0165168421002620
https://arxiv.org/abs/2102.08083
https://doi.org/10.1016/j.sigpro.2021.108225
http://doi.org/10.1016/j.sigpro.2021.108225
https://www.sciencedirect.com/science/article/pii/S0165168421002620
https://dblp.uni-trier.de/db/journals/corr/corr2102.html#abs-2102-08083