meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting

Code

• github.com/jklj077/meProp
  OfficialIn paperpytorch★ 0
• github.com/lancopku/meProp
  OfficialIn paperpytorch★ 0

Abstract

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. The code is available at https://github.com/lancopku/meProp

Tasks

Reproductions