Results 11 to 20 of about 120,084 (296)
Developing normalization schemes for data isolated distributed deep learning [PDF]
IET Cyber-Physical Systems, 2021 Distributed deep learning is an important and indispensable direction in the field of deep learning research. Earlier research has proposed many algorithms or techniques on accelerating distributed neural network training.
Yujue Zhou, Ligang He, Shuang‐Hua Yang
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A Survey From Distributed Machine Learning to Distributed Deep Learning
CoRR, 2023 Artificial intelligence has made remarkable progress in handling complex tasks, thanks to advances in hardware acceleration and machine learning algorithms. However, to acquire more accurate outcomes and solve more complex issues, algorithms should be trained with more data.
Mohammad Dehghani, Zahra Yazdanparast
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Collective Communication Performance Evaluation for Distributed Deep Learning Training
Applied SciencesIn distributed deep learning, the improper use of the collective communication library can lead to a decline in deep learning performance due to increased communication time.
Sookwang Lee, Jaehwan Lee
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Communication Optimization Schemes for Accelerating Distributed Deep Learning Systems
Applied Sciences, 2020 In a distributed deep learning system, a parameter server and workers must communicate to exchange gradients and parameters, and the communication cost increases as the number of workers increases.
Jaehwan Lee +4 more
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Applied Sciences, 2020 To accommodate lots of training data and complex training models, “distributed” deep learning training has become employed more and more frequently. However, communication bottlenecks between distributed systems lead to poor performance of distributed ...
Junghoon Woo +2 more
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Algorithms for Efficient and Robust Distributed Deep Learning
, 2022 The success of deep learning may be attributed in large part to remarkable growth in the size and complexity of deep neural networks. However, present learning systems raise significant efficiency concerns and privacy: (1) currently, training systems are lagging behind the fast growth of deep neural architectures, and the training efficiency of deep ...
Lin, Tao
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Deep Stable Learning for Out-Of-Distribution Generalization [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 Approaches based on deep neural networks have achieved striking performance when testing data and training data share similar distribution, but can significantly fail otherwise. Therefore, eliminating the impact of distribution shifts between training and testing data is crucial for building performance-promising deep models.
Xingxuan Zhang +5 more
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SoftwareX, 2023 In this paper, a distributed deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO) is proposed for an unmanned aerial vehicle (UAV) to autonomously track another UAV. Accordingly, this paper makes three important contributions
Ziya Tan, Mehmet Karaköse
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Recommender System for Optimal Distributed Deep Learning in Cloud Datacenters [PDF]
, 2021 With the modern advancements in Deep Learning architectures, and abundant research consistently being put forward in areas such as computer vision, natural language processing and forecasting.
Doyle, J +3 more
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From distributed machine to distributed deep learning: a comprehensive survey
Journal of Big Data, 2023 Artificial intelligence has made remarkable progress in handling complex tasks, thanks to advances in hardware acceleration and machine learning algorithms.
Mohammad Dehghani, Zahra Yazdanparast
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