most citedSpeeding up Convolutional Neural Networks By Exploiting the Sparsity of Rectifier Units

33 citations · 43 across the 5 of their papers we have counts for

collaborators

5 papers

cs.DC20178 cited

Performance Evaluation of Deep Learning Tools in Docker Containers

Pengfei Xu, Shaohuai Shi, Xiaowen Chu

With the success of deep learning techniques in a broad range of application domains, many deep learning software frameworks have been developed and are being updated frequently to…

cs.CV201733 cited

Speeding up Convolutional Neural Networks By Exploiting the Sparsity of Rectifier Units

Shaohuai Shi, Xiaowen Chu

Rectifier neuron units (ReLUs) have been widely used in deep convolutional networks. An ReLU converts negative values to zeros, and does not change positive values, which leads to…

cs.DC2017

Supervised Learning Based Algorithm Selection for Deep Neural Networks

Shaohuai Shi, Pengfei Xu, Xiaowen Chu

Many recent deep learning platforms rely on third-party libraries (such as cuBLAS) to utilize the computing power of modern hardware accelerators (such as GPUs). However, we observ…

cs.NI20151 cited

Efficient Channel-Hopping Rendezvous Algorithm Based on Available Channel Set

Lu Yu, Hai Liu, Yiu-Wing Leung +2

In cognitive radio networks, rendezvous is a fundamental operation by which two cognitive users establish a communication link on a commonly-available channel for communications. S…

cs.NI20151 cited

ZOS: A Fast Rendezvous Algorithm Based on Set of Available Channels for Cognitive Radios

Zhiyong Lin, Hai Liu, Lu Yu +2

Most of existing rendezvous algorithms generate channel-hopping sequences based on the whole channel set. They are inefficient when the set of available channels is a small subset…