14 citations · 41 across the 7 of their papers we have counts for
8 papers
NAS-LID: Efficient Neural Architecture Search with Local Intrinsic Dimension
Xin He, Jiangchao Yao, Yuxin Wang +5
One-shot neural architecture search (NAS) substantially improves the search efficiency by training one supernet to estimate the performance of every possible child architecture (i.…
A Quantitative Survey of Communication Optimizations in Distributed Deep Learning
Shaohuai Shi, Zhenheng Tang, Xiaowen Chu +3
Nowadays, large and complex deep learning (DL) models are increasingly trained in a distributed manner across multiple worker machines, in which extensive communications between wo…
Communication-Efficient Decentralized Learning with Sparsification and Adaptive Peer Selection
Zhenheng Tang, Shaohuai Shi, Xiaowen Chu
Distributed learning techniques such as federated learning have enabled multiple workers to train machine learning models together to reduce the overall training time. However, cur…
Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models
Xin He, Shihao Wang, Shaohuai Shi +10
Skin disease is one of the most common types of human diseases, which may happen to everyone regardless of age, gender or race. Due to the high visual diversity, human diagnosis hi…
Layer-wise Adaptive Gradient Sparsification for Distributed Deep Learning with Convergence Guarantees
Shaohuai Shi, Zhenheng Tang, Qiang Wang +2
To reduce the long training time of large deep neural network (DNN) models, distributed synchronous stochastic gradient descent (S-SGD) is commonly used on a cluster of workers. Ho…
Benchmarking the Performance and Energy Efficiency of AI Accelerators for AI Training
Yuxin Wang, Qiang Wang, Shaohuai Shi +4
Deep learning has become widely used in complex AI applications. Yet, training a deep neural network (DNNs) model requires a considerable amount of calculations, long running time,…