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Park, J. (Jihong)   Saad, W. (Walid)   Auswahl aufheben

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    Federated learning on the road autonomous controller design for connected and autonomous vehicles

    Freier Zugriff
    Zeng, T. (Tengchan) / Semiari, O. (Omid) / Chen, M. (Mingzhe) et al. | BASE | 2022
    Schlagwörter: machine learning

    Proxy experience replay:federated distillation for distributed reinforcement learning

    Freier Zugriff
    Cha, H. (Han) / Park, J. (Jihong) / Kim, H. (Hyesung) et al. | BASE | 2020
    Schlagwörter: Machine learning

    Predictive closed-loop remote control over wireless two-way split Koopman autoencoder

    Freier Zugriff
    Girgis, A. M. (Abanoub M.) / Seo, H. (Hyowoon) / Park, J. (Jihong) et al. | BASE | 2022
    Schlagwörter: split learning

    Massive autonomous UAV path planning:a neural network based mean-field game theoretic approach

    Freier Zugriff
    Shiri, H. (Hamid) / Park, J. (Jihong) / Bennis, M. (Mehdi) | BASE | 2019
    Schlagwörter: Machine learning

    Integrating LEO satellites and multi-UAV reinforcement learning for hybrid FSO/RF non-terrestrial networks

    Freier Zugriff
    Lee, J.-H. (Ju-Hyung) / Park, J. (Jihong) / Bennis, M. (Mehdi) et al. | BASE | 2022
    Schlagwörter: multi-agent deep reinforcement learning

    Ultra-reliable indoor millimeter wave communications using multiple artificial intelligence-powered intelligent surfaces

    Freier Zugriff
    Naderi Soorki, M. (Mehdi) / Saad, W. (Walid) / Bennis, M. (Mehdi) et al. | BASE | 2021
    Schlagwörter: deep risk-sensitive reinforcement learning

    Cyber-physical security and safety of autonomous connected vehicles:optimal control meets multi-armed bandit learning

    Freier Zugriff
    Ferdowsi, A. (Aidin) / Ali, S. (Samad) / Saad, W. (Walid) et al. | BASE | 2019
    Schlagwörter: multi-armed bandit learning

    Vehicular cooperative perception through action branching and federated reinforcement learning

    Freier Zugriff
    Abdel-Aziz, M. K. (Mohamed K.) / Perfecto, C. (Cristina) / Samarakoon, S. (Sumudu) et al. | BASE | 2022
    Schlagwörter: federated reinforcement learning

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