Výsledky vyhledávání - Aautoencoder

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    Fusing Heterogeneous Features From Stacked Sparse Autoencoder for Histopathological Image Analysis Autor Zhang, Xiaofan, Dou, Hang, Ju, Tao, Xu, Jun, Zhang, Shaoting

    ISSN: 2168-2194, 2168-2208, 2168-2208
    Vydáno: United States IEEE 01.09.2016
    “… from the cell detection results by a stacked sparse autoencoder. Because of the dramatically different characteristics and representations of these heterogeneous features (i.e…”
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    DDSA: A Defense Against Adversarial Attacks Using Deep Denoising Sparse Autoencoder Autor Bakhti, Yassine, Fezza, Sid Ahmed, Hamidouche, Wassim, Deforges, Olivier

    ISSN: 2169-3536, 2169-3536
    Vydáno: Piscataway IEEE 2019
    Vydáno v IEEE access (2019)
    “… In this paper, we propose a novel defense solution based on a Deep Denoising Sparse Autoencoder (DDSA…”
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    Attention-based Wavenet Autoencoder for Universal Voice Conversion Autor Polyak, Adam, Wolf, Lior

    ISSN: 2379-190X
    Vydáno: IEEE 01.05.2019
    “…We present a method for converting any voice to a target voice. The method is based on a WaveNet autoencoder, with the addition of a novel attention component…”
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    Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning Autor Sun, Jiedi, Yan, Changhong, Wen, Jiangtao

    ISSN: 0018-9456, 1557-9662
    Vydáno: New York IEEE 01.01.2018
    “…Effective intelligent fault diagnosis has long been a research focus on the condition monitoring of rotary machinery systems. Traditionally, time-domain…”
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    Hybrid Deep Learning for Botnet Attack Detection in the Internet-of-Things Networks Autor Popoola, Segun I., Adebisi, Bamidele, Hammoudeh, Mohammad, Gui, Guan, Gacanin, Haris

    ISSN: 2327-4662, 2327-4662
    Vydáno: Piscataway IEEE 15.03.2021
    Vydáno v IEEE internet of things journal (15.03.2021)
    “… In this article, we reduce the feature dimensionality of large-scale IoT network traffic data using the encoding phase of long short-term memory autoencoder (LAE…”
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    An ensemble learning framework for anomaly detection in building energy consumption Autor Araya, Daniel B., Grolinger, Katarina, ElYamany, Hany F., Capretz, Miriam A.M., Bitsuamlak, Girma

    ISSN: 0378-7788, 1872-6178
    Vydáno: Lausanne Elsevier B.V 01.06.2017
    Vydáno v Energy and buildings (01.06.2017)
    “…During building operation, a significant amount of energy is wasted due to equipment and human-related faults. To reduce waste, today's smart buildings monitor…”
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    Phrase embedding learning from internal and external information based on autoencoder Autor Li, Rongsheng, Yu, Qinyong, Huang, Shaobin, Shen, Linshan, Wei, Chi, Sun, Xuewei

    ISSN: 0306-4573, 1873-5371
    Vydáno: Oxford Elsevier Ltd 01.01.2021
    Vydáno v Information processing & management (01.01.2021)
    “…•We propose an autoencoder-based method to generate phrase embedding.•The method uses full connected network, LSTM and attention to get phrase embedding.•The…”
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    Deep learning enabled Lagrangian particle trajectory simulation Autor Gan, Jingwei, Liu, Pai, Chakrabarty, Rajan K.

    ISSN: 0021-8502, 1879-1964
    Vydáno: Elsevier Ltd 01.01.2020
    Vydáno v Journal of aerosol science (01.01.2020)
    “… – the variational autoencoder (VAE) and the generative adversarial net (GAN). The performance of both models was then benchmarked according to statistical analysis…”
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    Leveraging latent representations for milk yield prediction and interpolation using deep learning Autor Liseune, Arno, Salamone, Matthieu, Van den Poel, Dirk, Van Ranst, Bonifacius, Hostens, Miel

    ISSN: 0168-1699, 1872-7107
    Vydáno: Amsterdam Elsevier B.V 01.08.2020
    “…•Inferring milk yields along the entire lactation curve by using deep learning.•Extracting a latent representation of a lactation curve with autoencoders…”
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    Extended Pipeline for Content-Based Feature Engineering in Music Genre Recognition Autor Raissi, Tina, Tibo, Alessandro, Bientinesi, Paolo

    ISSN: 2379-190X
    Vydáno: IEEE 01.04.2018
    “…, determined by an autoencoder. The results of the experiments conducted on GTZAN dataset reveal a noticeable contribution of this methodology towards the model's…”
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    Dynamic movement primitives in latent space of time-dependent variational autoencoders Autor Nutan Chen, Karl, Maximilian, van der Smagt, Patrick

    ISSN: 2164-0580
    Vydáno: IEEE 01.11.2016
    “… In this paper, we explore a method that embeds DMPs into the latent space of a time-dependent variational autoencoder framework…”
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    Sensors for Gait, Posture, and Health Monitoring Volume 2

    ISBN: 9783039363445, 3039363441, 9783039363452, 303936345X
    Vydáno: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
    “…In recent years, many technologies for gait and posture assessments have emerged. Wearable sensors, active and passive in-house monitors, and many combinations…”
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    E-kniha
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    Sensors for Gait, Posture, and Health Monitoring Volume 3

    ISBN: 9783039363476, 3039363476, 3039363468, 9783039363469
    Vydáno: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
    “…In recent years, many technologies for gait and posture assessments have emerged. Wearable sensors, active and passive in-house monitors, and many combinations…”
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    Sensors for Gait, Posture, and Health Monitoring Volume 1

    ISBN: 3039363433, 9783039363421, 9783039363438, 3039363425
    Vydáno: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
    “…In recent years, many technologies for gait and posture assessments have emerged. Wearable sensors, active and passive in-house monitors, and many combinations…”
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    E-kniha
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    Robustifying models against adversarial attacks by Langevin dynamics Autor Srinivasan, Vignesh, Rohrer, Csaba, Marban, Arturo, Müller, Klaus-Robert, Samek, Wojciech, Nakajima, Shinichi

    ISSN: 0893-6080, 1879-2782, 1879-2782
    Vydáno: United States Elsevier Ltd 01.05.2021
    Vydáno v Neural networks (01.05.2021)
    “… To achieve this task, we introduce a generative model of the conditional distribution of the inputs given labels that can be learned through a supervised Denoising Autoencoder (sDAE…”
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