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检索条件"主题词=variational autoencoder"
1537 条 记 录,以下是1331-1340 订阅
排序:
Towards a robust and reliable deep learning approach for detection of compact binary mergers in gravitational wave data
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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2023年 第4期4卷 045028页
作者: Jadhav, Shreejit Shrivastava, Mihir Mitra, Sanjit Interuniv Ctr Astron & Astrophys IUCAA Post Bag 4 Pune 411007 India Swinburne Univ Technol Ctr Astrophys & Supercomp Hawthorn Vic 3122 Australia ARC Ctr Excellence Gravitat Wave Discovery OzGrav Melbourne Australia Indian Inst Technol IIT Kharagpur India
The ability of deep learning (DL) approaches to learn generalised signal and noise models, coupled with their fast inference on GPUs, holds great promise for enhancing gravitational-wave (GW) searches in terms of spee... 详细信息
来源: 评论
node2hash: Graph aware deep semantic text hashing
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INFORMATION PROCESSING & MANAGEMENT 2020年 第6期57卷 102143-102143页
作者: Chaidaroon, Suthee Park, Dae Hoon Chang, Yi Fang, Yi Santa Clara Univ Santa Clara CA 95053 USA Huawei Res Amer Santa Clara CA USA Jilin Univ Changchun Peoples R China
Semantic hashing is an effective method for fast similarity search which maps high-dimensional data to a compact binary code that preserves the semantic information of the original data. Most existing text hashing app... 详细信息
来源: 评论
An Energy-efficient And Trustworthy Unsupervised Anomaly Detection Framework (EATU) for IIoT
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ACM TRANSACTIONS ON SENSOR NETWORKS 2022年 第4期18卷 56-56页
作者: Huang, Zijie Wu, Yulei Tempini, Niccolo Lin, Hui Yin, Hao Univ Exeter Coll EMPS Dept Comp Sci Harrison BldgStreatham CampusN Pk Rd Exeter Devon England Univ Exeter Coll SSIS Dept Sociol Philosophy & Anthropol Amory BldgRennes Dr Exeter EX4 4RJ Devon England Fujian Normal Univ Coll Comp & Cyber Secur 8 Shangsan Rd Fuzhou 350117 Fujian Peoples R China Tsinghua Univ Res Inst Informat Technol Beijing 100084 Peoples R China
Many anomaly detection techniques have been adopted by Industrial Internet of Things (IIoT) for improving self-diagnosing efficiency and infrastructures security. However, they are usually associated with the issues o... 详细信息
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Prediction of microstructure evolution at the atomic scale by deep generative model in combination with recurrent neural networks
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ACTA MATERIALIA 2023年 第1期259卷
作者: Sase, Kohei Shibuta, Yasushi Univ Tokyo Dept Mat Engn 7-3-1 HongoBunkyo Ku Tokyo 1138656 Japan
A novel method to predict multi-atom cooperative phenomena at atomic scale is proposed based on a deep generative model in combination with recurrent neural network. The variational autoencoder (VAE) model successfull... 详细信息
来源: 评论
Highly Productive 3D Printing Process to Transcend Intractability in Materials and Geometries via Interactive Machine-Learning-Based Technique
ADVANCED INTELLIGENT SYSTEMS
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ADVANCED INTELLIGENT SYSTEMS 2023年 第7期5卷
作者: Kim, Yuseok Park, Suk Hee Pusan Natl Univ Sch Mech Engn Busan 46241 South Korea
Herein, a highly productive and defect-free 3D-printing system enforced by deep-learning (DL)-based anomaly detection and reinforcement-learning (RL)-based optimization processes is developed. Unpredictable defect fac... 详细信息
来源: 评论
Synthesis of Synthetic Hyperspectral Images with Controllable Spectral Variability Using a Generative Adversarial Network
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REMOTE SENSING 2023年 第16期15卷 3919-3919页
作者: Palsson, Burkni Ulfarsson, Magnus O. Sveinsson, Johannes R. Univ Iceland Fac Elect & Comp Engn IS-105 Reykjavik Iceland
In hyperspectral unmixing (HU), spectral variability in hyperspectral images (HSIs) is a major challenge which has received a lot of attention over the last few years. Here, we propose a method utilizing a generative ... 详细信息
来源: 评论
variational data augmentation for a learning-based granular predictive model of
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ELECTRIC POWER SYSTEMS RESEARCH 2024年 232卷
作者: Zhao, Tianqiao Yue, Meng Jensen, Michael Endo, Satoshi Marschilok, Amy C. Nugent, Brian Cerruti, Brian Spanos, Constantine Brookhaven Natl Lab Upton NY 11973 USA Orange & Rockland Util Inc Pearl River NY 10965 USA Con Edison Co New York NY USA
As the trend in climate change continues, extreme weather events are expected to occur with increasing frequency and severity and pose a significant threat to the electric power infrastructure. Regardless of the effor... 详细信息
来源: 评论
An automatic classification framework for identifying type of plant leaf diseases using multi-scale feature fusion-based adaptive deep network
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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 第PartA期95卷
作者: Nagachandrika, Bathula Prasath, R. Joe, I. R. Praveen KCG Coll Technol Dept Comp Sci & Engn Chennai 600097 Tamil Nadu India Vellore Inst Technol Comp Sci & Engn Tiruvalam Rd Vellore 632014 Tamil Nadu India
This method of identifying plant leaf disease generally involves a large team of experts with extensive knowledge of plant diseases, and it can be expensive, time-consuming, and subjective. Hence, a novel plant leaf d... 详细信息
来源: 评论
Online training of deep neural networks for classification
Online training of deep neural networks for classification
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作者: Tumpach, Jiří Charles University of Prague
Deep learning is usually applied to static datasets. If used for classification based on data streams, it is not easy to take into account a non-stationarity. This thesis presents work in progress on a new method for ... 详细信息
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Energy Theft Detection Model Based on VAE-GAN for Imbalanced Dataset
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ENERGIES 2023年 第3期16卷 1109-1109页
作者: Sun, Youngghyu Lee, Jiyoung Kim, Soohyun Seon, Joonho Lee, Seongwoo Kyeong, Chanuk Kim, Jinyoung Kwangwoon Univ Dept Elect Convergence Engn Seoul 01897 South Korea
Energy theft causes a lot of economic losses every year. In the practical environment of energy theft detection, it is required to solve imbalanced data problem where normal user data are significantly larger than ene... 详细信息
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