Výsledky vyhľadávania - "Quantized Autoencoder"

CLIP-FSQAE: Clip-Guided Finite Scalar Quantized Autoencoder for Few-Shot Anomaly Detection

Autori: Shih-Chih Lin Shang-Hong Lai

Zdroj: 2025 IEEE International Conference on Image Processing (ICIP). :313-318

Tackling the Nonlinearity Problem in Inverse Modeling: Mixture Density Network-Backed Quantized AutoEncoder

Autori: Muhammed Nur Talha Kilic Yuwei Mao Vishu Gupta a ďalší

Zdroj: 2024 International Conference on Machine Learning and Applications (ICMLA). :997-1000

VQ-DcTr: Vector-Quantized Autoencoder With Dual-channel Transformer Points Splitting for 3D Point Cloud Completion

Autori: Ben Fei Weidong Yang Wen-Ming Chen a ďalší

Zdroj: Proceedings of the 30th ACM International Conference on Multimedia. :4769-4778

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

Quantized autoencoder (QAE) intrusion detection system for anomaly detection in resource-constrained IoT devices using RT-IoT2022 dataset

Autori: B S Sharmila Rohini Nagapadma

Zdroj: Cybersecurity, Vol 6, Iss 1, Pp 1-15 (2023)

Predmety: Computer engineering. Computer hardware, TK7885-7895, Electronic computers. Computer science, QA75.5-76.95

Popis súboru: electronic resource

Relation: https://doaj.org/toc/2523-3246

Prístupová URL adresa: https://doaj.org/article/3b849a72ef8f4ddcb8564d125f9bc097

A Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent Flow

Autori: Momenifar, Mohammadreza Diao, Enmao Tarokh, Vahid a ďalší

Zdroj: 2022 Data Compression Conference (DCC). :01-10

Predmety: FOS: Computer and information sciences, Computer Science - Machine Learning, 0103 physical sciences, Fluid Dynamics (physics.flu-dyn), FOS: Physical sciences, Physics - Fluid Dynamics, 01 natural sciences, Machine Learning (cs.LG)

Prístupová URL adresa: http://arxiv.org/abs/2201.03617

Vector-Quantized Autoencoder With Copula for Collaborative Filtering

Autori: Yong Wang Ting Zhong Kunpeng Zhang a ďalší

Zdroj: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. :3458-3462

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

Prístupová URL adresa: https://dl.acm.org/doi/pdf/10.1145/3459637.3482216
https://dblp.uni-trier.de/db/conf/cikm/cikm2021.html#WangZXZZW21

Vector-Quantized Autoencoder with Copula for Collaborative Filtering

Autori: Wang, Guanyu Zhong, Ting Xu, Xovee a ďalší

Témy: Collaborative filtering, Gaussian copula, Recommender system, Variational autoencoder, Vector quantisation, Conference paper

URL: http://repository.ust.hk/ir/Record/1783.1-114828
http://lbdiscover.ust.hk/uresolver?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rfr_id=info:sid/HKUST:SPI&rft.genre=article&rft.issn=&rft.volume=&rft.issue=&rft.date=2021&rft.spage=3458&rft.aulast=Wang&rft.aufirst=&rft.atitle=Vector-Quantized+Autoencoder+with+Copula+for+Collaborative+Filtering&rft.title=International+Conference+on+Information+and+Knowledge+Management%2C+Proceedings
http://www.scopus.com/record/display.url?eid=2-s2.0-85119208580&origin=inward

VQ-HPS: Human Pose and Shape Estimation in a Vector-Quantized Latent Space

Autori: Fiche, Guénolé Leglaive, Simon Alameda-Pineda, Xavier a ďalší

Prispievatelia: Fiche, Guénolé Leglaive, Simon Alameda-Pineda, Xavier a ďalší

Zdroj: Lecture Notes in Computer Science ISBN: 9783031729423
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Digital.CSIC. Repositorio Institucional del CSIC
Consejo Superior de Investigaciones Científicas (CSIC)

Predmety: FOS: Computer and information sciences, Classificació INSPEC::Pattern recognition::Computer vision, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Human mesh recovery, Human pose and shape estimation, Transformers, Computer Vision and Pattern Recognition (cs.CV), Àrees temàtiques de la UPC::Informàtica::Automàtica i control, Computer Science - Computer Vision and Pattern Recognition, Vector quantized autoencoder

Popis súboru: application/pdf

Prístupová URL adresa: http://arxiv.org/abs/2312.08291
https://api.elsevier.com/content/abstract/scopus_id/85211356626
http://hdl.handle.net/10261/388303
https://hal.science/hal-04644818v1
https://doi.org/10.1007/978-3-031-72943-0_27

Quantized autoencoder (QAE) intrusion detection system for anomaly detection in resource-constrained IoT devices using RT-IoT2022 dataset.

Autori: Sharmila, B S Nagapadma, Rohini

Zdroj: Cybersecurity (2523-3246); 9/5/2023, Vol. 6 Issue 1, p1-15, 15p

Predmety: ANOMALY detection (Computer security), INTERNET of things, ARTIFICIAL intelligence

GPTCast: a weather language model for precipitation nowcasting.

Autori: Franch, Gabriele Tomasi, Elena Wanjari, Rishabh a ďalší

Zdroj: Geoscientific Model Development; 2025, Vol. 18 Issue 16, p5351-5371, 21p

Predmety: PRECIPITATION forecasting, WEATHER forecasting, RADAR meteorology, SPATIOTEMPORAL processes, MEASUREMENT uncertainty (Statistics), GENERATIVE artificial intelligence

Geografický termín: EMILIA-Romagna (Italy)

Harnessing the Power of Pre-Trained Models for Efficient Semantic Communication of Text and Images †.

Autori: Kutay, Emrecan Yener, Aylin

Zdroj: Entropy; Aug2025, Vol. 27 Issue 8, p813, 25p

Predmety: VECTOR quantization, ENCODING, MACHINE-to-machine communications, COMPUTER performance, MACHINE learning, MULTIMODAL user interfaces, ARTIFICIAL neural networks

Advancing Blind Face Restoration: Robustness and Identity Preservation with Integrated GAN and Codebook Prior Architectures

Autori: Tayarani Bathaie, Seyed Nima An, Aijun

Prispievatelia: Tayarani Bathaie, Seyed Nima An, Aijun

Predmety: Blind face restoration (BFR), Identity preservation, Generative adversarial networks (GANs), StyleGAN, Vector Quantized AutoEncoder, CelebA-IntenseTest

Popis súboru: application/pdf

Relation: https://hdl.handle.net/10315/41866

Dostupnosť: https://hdl.handle.net/10315/41866

QAE-IDS: DDoS anomaly detection in IoT devices using Post-Quantization Training.

Autori: Sharmila, B. S. Nagapadma, Rohini

Zdroj: Smart Science; Dec2023, Vol. 11 Issue 4, p774-789, 16p

Predmety: ANOMALY detection (Computer security), INTERNET of things, DENIAL of service attacks, ARTIFICIAL intelligence, INTRUSION detection systems (Computer security)

Speech Watermarking with Discrete Intermediate Representations

Autori: Ji, Shengpeng Jiang, Ziyue Zuo, Jialong a ďalší

Témy: Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Signal Processing, text

URL: http://arxiv.org/abs/2412.13917

GPD-1: Generative Pre-training for Driving

Autori: Xie, Zixun Zuo, Sicheng Zheng, Wenzhao a ďalší

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

URL: http://arxiv.org/abs/2412.08643

VQalAttent: a Transparent Speech Generation Pipeline based on Transformer-learned VQ-VAE Latent Space

Autori: Rodriguez, Armani Kokalj-Filipovic, Silvija

Témy: Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing, text

URL: http://arxiv.org/abs/2411.14642

Classification-Oriented Semantic Wireless Communications

Autori: Kutay, Emrecan Yener, Aylin

Témy: Computer Science - Information Theory, text

URL: http://arxiv.org/abs/2401.18069

Learning the Language of Protein Structure

Autori: Gaujac, Benoit Donà, Jérémie Copoiu, Liviu a ďalší

Témy: Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2405.15840

Time Cell Inspired Temporal Codebook in Spiking Neural Networks for Enhanced Image Generation

Autori: Feng, Linghao Zhao, Dongcheng Shen, Sicheng a ďalší

Témy: Computer Science - Neural and Evolutionary Computing, text

URL: http://arxiv.org/abs/2405.14474

From Minimal Traces to Scenarios of the Past: A Neuro-Computational Model on Regaining Categoricity and Compositionality in Remembering

Autori: Righetti, Francesca Fayyaz, Zahra Wiskott, Laurenz a ďalší

Zdroj: Proceedings of the Annual Meeting of the Cognitive Science Society. 47

Predmety: Concepts and categories, Memory, Computational Modeling, Neural Networks

Popis súboru: application/pdf

Prístupová URL adresa: https://escholarship.org/uc/item/66g451gj
https://escholarship.org/content/qt66g451gj/qt66g451gj.pdf