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meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting
Xu SUN · Xuancheng REN · Shuming Ma · Houfeng Wang

Mon Aug 07 01:30 AM -- 05:00 AM (PDT) @ Gallery #17
We propose a simple yet effective technique for neural network learning. The forward propagation is computed as usual. In back propagation, only a small subset of the full gradient is computed to update the model parameters. The gradient vectors are sparsified in such a way that only the top-$k$ elements (in terms of magnitude) are kept. As a result, only $k$ rows or columns (depending on the layout) of the weight matrix are modified, leading to a linear reduction ($k$ divided by the vector dimension) in the computational cost. Surprisingly, experimental results demonstrate that we can update only 1--4\% of the weights at each back propagation pass. This does not result in a larger number of training iterations. More interestingly, the accuracy of the resulting models is actually improved rather than degraded, and a detailed analysis is given.

Author Information

Xu SUN (Peking University)
Xuancheng REN (Peking University)
Shuming Ma (Peking University)
Houfeng Wang

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