1.7k citations · 1.8k across the 19 of their papers we have counts for
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IRS: A Large Naturalistic Indoor Robotics Stereo Dataset to Train Deep Models for Disparity and Surface Normal Estimation
Qiang Wang, Shizhen Zheng, Qingsong Yan +3
Indoor robotics localization, navigation, and interaction heavily rely on scene understanding and reconstruction. Compared to the monocular vision which usually does not explicitly…
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,…
AutoML: A Survey of the State-of-the-Art
Xin He, Kaiyong Zhao, Xiaowen Chu
Deep learning (DL) techniques have penetrated all aspects of our lives and brought us great convenience. However, building a high-quality DL system for a specific task highly relie…
A Distributed Synchronous SGD Algorithm with Global Top- Sparsification for Low Bandwidth Networks
Shaohuai Shi, Qiang Wang, Kaiyong Zhao +4
Distributed synchronous stochastic gradient descent (S-SGD) has been widely used in training large-scale deep neural networks (DNNs), but it typically requires very high communicat…