Shaohuai Shi

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Hong Kong Baptist University

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Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models

Skin disease is one of the most common types of human diseases, which ma...
11/20/2019 ∙ by Xin He, et al. ∙ 18 ∙ share

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Performance Modeling and Evaluation of Distributed Deep Learning Frameworks on GPUs

Deep learning frameworks have been widely deployed on GPU servers for de...
11/16/2017 ∙ by Shaohuai Shi, et al. ∙ 0 ∙ share

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Modeling and Evaluation of Synchronous Stochastic Gradient Descent in Distributed Deep Learning on Multiple GPUs

With huge amounts of training data, deep learning has made great breakth...
05/10/2018 ∙ by Shaohuai Shi, et al. ∙ 0 ∙ share

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Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes

Synchronized stochastic gradient descent (SGD) optimizers with data para...
07/30/2018 ∙ by Xianyan Jia, et al. ∙ 0 ∙ share

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MG-WFBP: Efficient Data Communication for Distributed Synchronous SGD Algorithms

Distributed synchronous stochastic gradient descent has been widely used...
11/27/2018 ∙ by Shaohuai Shi, et al. ∙ 0 ∙ share

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Performance Evaluation of Deep Learning Tools in Docker Containers

With the success of deep learning techniques in a broad range of applica...
11/09/2017 ∙ by Pengfei Xu, et al. ∙ 0 ∙ share

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A Distributed Synchronous SGD Algorithm with Global Top-k Sparsification for Low Bandwidth Networks

Distributed synchronous stochastic gradient descent (S-SGD) with data pa...
01/14/2019 ∙ by Shaohuai Shi, et al. ∙ 0 ∙ share

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Performance and Power Evaluation of AI Accelerators for Training Deep Learning Models

Deep neural networks (DNNs) have become widely used in many AI applicati...
09/15/2019 ∙ by Yuxin Wang, et al. ∙ 0 ∙ share

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Layer-wise Adaptive Gradient Sparsification for Distributed Deep Learning with Convergence Guarantees

To reduce the long training time of large deep neural network (DNN) mode...
11/20/2019 ∙ by Shaohuai Shi, et al. ∙ 0 ∙ share

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Understanding Top-k Sparsification in Distributed Deep Learning

Distributed stochastic gradient descent (SGD) algorithms are widely depl...
11/20/2019 ∙ by Shaohuai Shi, et al. ∙ 0 ∙ share

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MG-WFBP: Merging Gradients Wisely for Efficient Communication in Distributed Deep Learning

Distributed synchronous stochastic gradient descent has been widely used...
12/18/2019 ∙ by Shaohuai Shi, et al. ∙ 0 ∙ share

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Communication-Efficient Distributed Deep Learning: A Comprehensive Survey

Distributed deep learning becomes very common to reduce the overall trai...
03/10/2020 ∙ by Zhenheng Tang, et al. ∙ 0 ∙ share

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FADNet: A Fast and Accurate Network for Disparity Estimation

Deep neural networks (DNNs) have achieved great success in the area of c...
03/24/2020 ∙ by Qiang Wang, et al. ∙ 0 ∙ share

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Communication Contention Aware Scheduling of Multiple Deep Learning Training Jobs

Distributed Deep Learning (DDL) has rapidly grown its popularity since i...
02/24/2020 ∙ by Qiang Wang, et al. ∙ 0 ∙ share

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Communication-Efficient Decentralized Learning with Sparsification and Adaptive Peer Selection

Distributed learning techniques such as federated learning have enabled ...
02/22/2020 ∙ by Zhenheng Tang, et al. ∙ 0 ∙ share

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