176 citations · 339 across the 9 of their papers we have counts for
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stat.ML2017★ 12 cited
BT-Nets: Simplifying Deep Neural Networks via Block Term Decomposition
Guangxi Li, Jinmian Ye, Haiqin Yang +3
Recently, deep neural networks (DNNs) have been regarded as the state-of-the-art classification methods in a wide range of applications, especially in image classification. Despite…
cs.LG2017★ 17 cited
Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition
Jinmian Ye, Linnan Wang, Guangxi Li +4
Recurrent Neural Networks (RNNs) are powerful sequence modeling tools. However, when dealing with high dimensional inputs, the training of RNNs becomes computational expensive due…