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Differentially Private Learning of Distributed Deep Models
Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, 2020This study presents an optimal differential privacy framework for learning of distributed deep models. The deep models, consisting of a nested composition of mappings, are learned analytically in a private setting using variational optimization methodology.
Mohit Kumar 0001 +3 more
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Coded Parallelism for Distributed Deep Learning
2023 IEEE International Symposium on Information Theory (ISIT), 2023Songting Ji +4 more
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Distributional Deep Reinforcement Learning with a Mixture of Gaussians
2019 International Conference on Robotics and Automation (ICRA), 2019In this paper, we propose a novel distributional reinforcement learning (RL) method which models the distribution of the sum of rewards using a mixture density network. Recently, it has been shown that modeling the randomness of the return distribution leads to better performance in Atari games and control tasks.
Yunho Choi +2 more
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A Service Management Method for Distributed Deep Learning
2021 International Conference on Information and Communication Technology Convergence (ICTC), 2021Seung Woo Kum, Seungtaek Oh, Jaewon Moon
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Collaborative deep learning framework for fault diagnosis in distributed complex systems
Mechanical Systems and Signal Processing, 2021Haoxiang Wang +2 more
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Improving distributed video coding with deep learning
Journal of Electronic Imaging, 2023Djamel Eddine Boudechiche +2 more
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Demystifying Parallel and Distributed Deep Learning
ACM Computing Surveys, 2020Tal Ben-Nun, Torsten Hoefler
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A distributed deep reinforcement learning method for traffic light control
Neurocomputing, 2022Bo Liu, Zhengtao Ding
exaly