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cs.LG2021
Exploiting Elasticity in Tensor Ranks for Compressing Neural Networks
Jie Ran, Rui Lin, Hayden K. H. So +2
Elasticities in depth, width, kernel size and resolution have been explored in compressing deep neural networks (DNNs). Recognizing that the kernels in a convolutional neural netwo…
cs.LG2020
HOTCAKE: Higher Order Tucker Articulated Kernels for Deeper CNN Compression
Rui Lin, Ching-Yun Ko, Zhuolun He +5
The emerging edge computing has promoted immense interests in compacting a neural network without sacrificing much accuracy. In this regard, low-rank tensor decomposition constitut…