Zobrazeno 1 - 10 of 40 pro vyhledávání: '"Shaofeng Zhao"'
1
Akademický článek
An Approach for EEG Denoising Based on Wasserstein Generative Adversarial Network
Autor: Yuanzhe Dong, Xi Tang, Qingge Li, Yingying Wang, Naifu Jiang, Lan Tian, Yue Zheng, Xiangxin Li, Shaofeng Zhao, Guanglin Li, Peng Fang
Publikováno v: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 3524-3534 (2023)
Electroencephalogram (EEG) recordings often contain artifacts that would lower signal quality. Many efforts have been made to eliminate or at least minimize the artifacts, and most of them rely on visual inspection and manual operations, which is tim
Externí odkaz: https://doaj.org/article/37b2973c9af14b4ca635a7a77e46fe1f
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2
Akademický článek
The analgesic effect of different interactive modes of virtual reality: A prospective functional near-infrared spectroscopy (fNIRS) study
Autor: Xue Deng, Chuyao Jian, Qinglu Yang, Naifu Jiang, Zhaoyin Huang, Shaofeng Zhao
Publikováno v: Frontiers in Neuroscience, Vol 16 (2022)
Virtual reality has demonstrated its analgesic effectiveness. However, its optimal interactive mode for pain relief is yet unclear, with rare objective measurements that were performed to explore its neural mechanism.ObjectiveThis study primarily aim
Externí odkaz: https://doaj.org/article/41423123e6374d509a0a27fef96f62c3
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3
Akademický článek
Bridging the Gap Between Memory and Communication Efficiency on Distributed Deep Learning Systems
Autor: Shaofeng Zhao, Bo Liu, Fang Wang, Dan Feng
Publikováno v: IEEE Access, Vol 9, Pp 57075-57088 (2021)
Large-scale distributed deep learning is of great importance in various applications. For data-parallel distributed training systems, limited hardware resources (e.g., GPU memory and interconnection bandwidth) often become a performance bottleneck, a
Externí odkaz: https://doaj.org/article/122da6eca8484cccb868d254fe7074c6
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6
Bridging the Gap Between Memory and Communication Efficiency on Distributed Deep Learning Systems
Autor: Fang Wang, Dan Feng, Bo Liu, Shaofeng Zhao
Publikováno v: IEEE Access, Vol 9, Pp 57075-57088 (2021)
Large-scale distributed deep learning is of great importance in various applications. For data-parallel distributed training systems, limited hardware resources (e.g., GPU memory and interconnection bandwidth) often become a performance bottleneck, a
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5a15eb75c56452882b48037be39e62d7
https://ieeexplore.ieee.org/document/9398682/
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9
GradSA: Gradient Sparsification and Accumulation for Communication-Efficient Distributed Deep Learning
Autor: Bingsheng He, Wenbin Jiang, Shaofeng Zhao, Bo Liu, Hai Jin
Publikováno v: Green, Pervasive, and Cloud Computing ISBN: 9783030642426
GPC
Large-scale distributed deep learning is of great importance in various applications. For distributed training, the inter-node gradient communication often becomes the performance bottleneck. Gradient sparsification has been proposed to reduce the co
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0846f73ccf98471a43182a2c1569db2c
https://doi.org/10.1007/978-3-030-64243-3_6
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10
Layer-Centric Memory Reuse and Data Migration for Extreme-Scale Deep Learning on Many-Core Architectures
Autor: Wenbin Jiang, Shaofeng Zhao, Bingsheng He, Hai Jin, Bo Liu, Ma Yang, Xuanhua Shi
Publikováno v: ACM Transactions on Architecture and Code Optimization. 15:1-26
Due to the popularity of Deep Neural Network (DNN) models, we have witnessed extreme-scale DNN models with the continued increase of the scale in terms of depth and width. However, the extremely high memory requirements for them make it difficult to
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1e3d093b371d2cedd9f29314ae43af03
https://doi.org/10.1145/3243904
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