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检索条件"主题词=autoencoder"
4298 条 记 录,以下是1021-1030 订阅
APD: An autoencoder-based Prediction Model for Depression Diagnosis  22
APD: An Autoencoder-based Prediction Model for Depression Di...
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22nd IEEE International Conference on Information Reuse and Integration for Data Science (IRI)
作者: Park, Hyeseong Jung, Myung Won Raymond Oh, Uran AKA Cognit Corp Seoul South Korea Ewha Womans Univ Seoul South Korea
Depression is one of the most common mental health problems, which can lead to significant mental disorders and suicidal behavior. To diagnose depression levels, patients with depressive disorders are required to comp... 详细信息
来源: 评论
CONVOLUTIONAL autoencoder FOR UNSUPERVISED REPRESENTATION LEARNING OF POLSAR TIME-SERIES
CONVOLUTIONAL AUTOENCODER FOR UNSUPERVISED REPRESENTATION LE...
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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
作者: Di Martino, Thomas Guinvarc'h, Regis Thirion-Lefevre, Laetitia Koeniguer, Elise Colin Univ Paris Saclay Cent Supelec ONERA SONDRA F-91190 Gif Sur Yvette France Univ Paris Saclay ONERA Traitement Informat & Syst F-91123 Palaiseau France
Temporal Convolutional autoencoders are used as feature extractors to project time series onto a latent space where similarity detection can be easily performed. This model can generate accurate descriptors of the tem... 详细信息
来源: 评论
Machinery Fault Detection Using autoencoder and Online Sequential Extreme Learning Machine  7
Machinery Fault Detection Using Autoencoder and Online Seque...
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7th International Conference on Condition Monitoring of Machinery in Non-Stationary Operations (CMMNO)
作者: Yang, Zhe Long, Jianyu Cai, Xiaoman Li, Jianheng Li, Chuan Dongguan Univ Technol Sch Mech Engn Dongguan Peoples R China
Fault detection is one of the most challenging tasks in industrial applications, which aims at identifying the faulty condition deviating from the normal condition of the machine. In this work, a fault detection metho... 详细信息
来源: 评论
NMF-SAE: AN INTERPRETABLE SPARSE autoencoder FOR HYPERSPECTRAL UNMIXING
NMF-SAE: AN INTERPRETABLE SPARSE AUTOENCODER FOR HYPERSPECTR...
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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
作者: Xiong, Fengchao Zhou, Jun Ye, Minchao Lu, Jianfeng Qian, Yuntao Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China Griffith Univ Sch Informat & Commun Technol Brisbane Qld Australia China Jiliang Univ Coll Informat Engn Hangzhou Peoples R China Zhejiang Univ Coll Comp Sci Hangzhou Peoples R China
Hyperspectral unmixing is an important tool to learn the material constitution and distribution of a scene. Model-based unmixing methods depend on well-designed iterative optimization algorithms, which is usually time... 详细信息
来源: 评论
Convolutional autoencoder Based Textile Defect Detection Under Unconstrained Setting  20th
Convolutional Autoencoder Based Textile Defect Detection Und...
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20th International Conference on Artificial Intelligence and Soft Computing (ICAISC)
作者: Nagaraj, Deepak Vadiraja, Pramod Nalbach, Oliver Werth, Dirk AWS Inst Digitized Prod & Proc Saarbrucken Germany
Automated visual defect detection on textile products under unconstrained setting is a much sought-after, and at the same time a challenging problem. In general, textile products are structurally complex and highly va... 详细信息
来源: 评论
LogAttn: Unsupervised Log Anomaly Detection with an autoencoder Based Attention Mechanism  1
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14th International Conference on Knowledge Science, Engineering, and Management (KSEM)
作者: Zhang, Linming Li, Wenzhong Zhang, Zhijie Lu, Qingning Hou, Ce Hu, Peng Gui, Tong Lu, Sanglu Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China Huawei Nanjing Res Ctr Nanjing 210012 Peoples R China
System logs produced by modern computer systems are valuable resources for detecting anomalies, debugging performance issues, and recovering application failures. With the increasing scale and complexity of the log da... 详细信息
来源: 评论
Supervised Temporal autoencoder for Stock Return Time-series Forecasting  45
Supervised Temporal Autoencoder for Stock Return Time-series...
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45th Annual International IEEE-Computer-Society Computers, Software, and Applications Conference (COMPSAC)
作者: Wong, Steven Y. K. Chan, Jennifer S. K. Azizi, Lamiae Xu, Richard Y. D. Univ Technol Sydney Sch Elect & Data Engn Sydney NSW Australia Univ Sydney Sch Math & Stat Sydney NSW Australia
Financial markets are noisy learning environments. We propose an approach that regularizes the Temporal Convolutional Network using a supervised autoencoder, which we term the Supervised Temporal autoencoder (STAE). W... 详细信息
来源: 评论
Network Traffic Dynamics Prediction with a Hybrid Approach: autoencoder-VAR  7
Network Traffic Dynamics Prediction with a Hybrid Approach: ...
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7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)
作者: Gong, Xiaolin Ma, Tao Antoniou, Constantinos Tech Univ Munich Dept Civil Geo & Environm Engn Munich Germany
Network-wide traffic prediction is more effective for implementing traffic management control than traffic prediction for a single road. In order to improve the efficiency of network traffic forecasting, this research... 详细信息
来源: 评论
Low-Power Anomaly Detection and Classification System based on a Partially Binarized autoencoder for In-Sensor Computing  28
Low-Power Anomaly Detection and Classification System based ...
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28th IEEE International Conference on Electronics, Circuits, and Systems (IEEE ICECS)
作者: Vitolo, Paola Licciardo, Gian Domenico di Benedetto, Luigi Liguori, Rosalba Rubino, Alfredo Pau, Danilo Univ Salerno Dept Ind Engn I-84084 Fisciano SA Italy STMicroelectronics Syst Res & Applicat Agrate Brianza Italy
This work proposes a new ultra low-power fault detection system, suitable for extreme edge or in-sensor computing. The system is composed of a hybrid HW/SW architecture: a hardware auto-encoder (AE) is always on at th... 详细信息
来源: 评论
Impact of Different Compression Rates for Hyperspectral Data Compression Based on a Convolutional autoencoder  27
Impact of Different Compression Rates for Hyperspectral Data...
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Conference on Image and Signal Processing for Remote Sensing XXVII
作者: Kuester, Jannick Gross, Wolfgang Heizmann, Michael M. Middelmann, Wolfgang Fraunhofer IOSB Fraunhofer Ctr Machine Learning Ettlingen Germany Karlsruhe Inst Technol KIT Inst Ind Informat Technol IIIT Karlsruhe Germany
This work addresses the problem of hyperspectral data compression and compares the reconstruction accuracy for different compression rates. Through data compression, the enormous amount of data created by hyperspectra... 详细信息
来源: 评论