Výsledky vyhľadávania - "Distributed variational autoencoder"

DistVAE: Distributed Variational Autoencoder for sequential recommendation

Autori: Li Li Jianbing Xiahou Fan Lin a ďalší

Zdroj: Knowledge-Based Systems. 264:110313

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

Attribute-relevant distributed variational autoencoder integrated with LSTM for dynamic industrial soft sensing

Autori: Yan-Lin He Xing-Yuan Li Jia-Hui Ma a ďalší

Zdroj: Engineering Applications of Artificial Intelligence. 119:105737

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

Causal Variational Inference for Deconfounded Multi-Behavior Recommendation.

Autori: Chen, Yuzhe Cao, Jie Wang, Youquan a ďalší

Zdroj: ACM Transactions on Information Systems; Nov2025, Vol. 43 Issue 6, p1-26, 26p

Predmety: CAUSAL inference, AUTOENCODERS, SOCIAL influence, COMMON variable immunodeficiency, PREDICTION models

Dual-evolution: a deep sequence learning model exploring dual-side evolutions for movie recommendation.

Autori: Gan, Mingxin Zhang, Xiongtao Wang, Wenyu

Zdroj: Electronic Commerce Research; Aug2025, Vol. 25 Issue 4, p2795-2823, 29p

Predmety: RECOMMENDER systems, SEQUENTIAL learning, EVOLUTIONARY models, INTERNET entertainment, HUMAN behavior

A Wallboard Outsourcing Recommendation Method Based on Dual-Channel Neural Networks and Probabilistic Matrix Factorization.

Autori: Yang, Hongen Liu, Shanhui Cao, Yangzhen a ďalší

Zdroj: Electronics (2079-9292); Jul2025, Vol. 14 Issue 14, p2792, 17p

Predmety: RECOMMENDER systems, ARTIFICIAL neural networks, HYPOTHESIS, MATRIX decomposition, MANUFACTURING industries

A Novel Approach for Capturing Consumer Behavior and Preference - MulVAEK.

Autori: Li, Yijing Tang, Jing Gonul, Sinan a ďalší

Zdroj: IEEE Computational Intelligence Magazine; May2025, Vol. 20 Issue 2, p33-44, 12p

基于深度学习与大语言模型的序列推荐研究进展. (Chinese)

Alternate Title: Research Progress on Sequence Recommendation Based on Deep Learning and Large Language Model. (English)

Autori: 徐凤如 李博涵 胥 帅

Zdroj: Journal of Frontiers of Computer Science & Technology; Feb2025, Vol. 19 Issue 2, p344-366, 23p

Learning Latent Distribution for Distinguishing Network Traffic in Intrusion Detection System

Autori: Vu, L Cao, VL Nguyen, QU a ďalší

Témy: Conference Proceeding

URL: http://hdl.handle.net/10453/135132
IEEE International Conference on Communications
10.1109/ICC.2019.8762015

The application of artificial intelligence-assisted technology in cultural and creative product design.

Autori: Liang, Jing

Zdroj: Scientific Reports; 12/28/2024, Vol. 14 Issue 1, p1-18, 18p

Predmety: ARTIFICIAL intelligence, REINFORCEMENT learning, CULTURAL intelligence, INFORMATION storage & retrieval systems, PRODUCT design

Deep Information Retention Network‐Enabled Data Modeling for Key Quality Indicator Prediction in the Chemical Industry.

Autori: Luo, Jiang Wang, Yalin Liu, Chenliang a ďalší

Zdroj: Journal of Chemometrics; Dec2024, Vol. 38 Issue 12, p1-17, 17p

Predmety: STATISTICAL learning, INDEPENDENT variables, AUTOENCODERS, DATA distribution, MANUFACTURING processes, DEEP learning

Dynamic Soft Sensor Model for Endpoint Carbon Content and Temperature in BOF Steelmaking Based on Adaptive Feature Matching Variational Autoencoder.

Autori: Liu, Zhaoxiang Liu, Hui Chen, Fugang a ďalší

Zdroj: Processes; Sep2024, Vol. 12 Issue 9, p1807, 33p

Predmety: BASIC oxygen furnaces, FEATURE extraction, MARGINAL distributions, DEEP learning, STEEL manufacture

Towards Differential Privacy in Sequential Recommendation: A Noisy Graph Neural Network Approach.

Autori: Hu, Wentao Fang, Hui

Zdroj: ACM Transactions on Knowledge Discovery from Data; Jun2024, Vol. 18 Issue 5, p1-21, 21p

Predmety: GRAPH neural networks, RECOMMENDER systems, PRIVACY

BMDF-SR: bidirectional multi-sequence decoupling fusion method for sequential recommendation.

Autori: Gao, Aohua Qin, Jiwei Ma, Chao a ďalší

Zdroj: Journal of Intelligent Information Systems; Apr2024, Vol. 62 Issue 2, p485-507, 23p

Predmety: FACTOR structure, FORECASTING

Novel spatiotemporal graph attention model for production prediction and energy structure optimization of propylene production processes.

Autori: Lin, Xiaoyong Li, Zihui Han, Yongming a ďalší

Zdroj: Computers & Chemical Engineering. Feb2024, Vol. 181, pN.PAG-N.PAG. 1p.

Predmety: *MANUFACTURING processes, *PROPENE, *PREDICTION models, *ADAPTIVE control systems, *CARBON emissions

Learning Latent Distribution for Distinguishing Network Traffic in Intrusion Detection System

Autori: Ly Vu Diep N. Nguyen Eryk Dutkiewicz a ďalší

Zdroj: ICC 2019 - 2019 IEEE International Conference on Communications (ICC). :1-6

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

Prístupová URL adresa: https://opus.lib.uts.edu.au/bitstream/10453/135132/4/850C1285-7E95-4870-BEC4-CA887D7EE687%20AM%20combined.pdf
https://ieeexplore.ieee.org/abstract/document/8762015
https://dblp.uni-trier.de/db/conf/icc/icc2019.html#VuCNNHD19

Artificial Intelligence and Soft Computing

Autori: Leszek Rutkowski Rafał Scherer Marcin Korytkowski a ďalší

Typ zdroja: eBook.

Kategórie: COMPUTERS / Artificial Intelligence / General

Web and Big Data : 8th International Joint Conference, APWeb-WAIM 2024, Jinhua, China, August 30 – September 1, 2024, Proceedings, Part II

Autori: Wenjie Zhang Anthony Tung Zhonglong Zheng a ďalší

Typ zdroja: eBook.

Predmet: Big data, Data structures (Computer science), Information theory, Application software, Image processing—Digital techniques, Computer vision, Data mining

Kategórie: COMPUTERS / Database Administration & Management, COMPUTERS / Business & Productivity Software / General, COMPUTERS / Image Processing, COMPUTERS / Artificial Intelligence / Computer Vision & Pattern Recognition, COMPUTERS / Data Science / Data Analytics, COMPUTERS / Information Theory, MATHEMATICS / Probability & Statistics / General

IDENTIFICATION OF ERROR CAUSES AT THE COMMAND LEVEL IN PROCESSES

TARGET-TO-CATALYST TRANSLATION NETWORKS