Position-based Scaled Gradient for Model Quantization and Sparse Training

22 May 2020Jangho KimKiYoon YooNojun Kwak

We propose the position-based scaled gradient (PSG) that scales the gradient depending on the position of a weight vector to make it more compression-friendly. First, we theoretically show that applying PSG to the standard gradient descent (GD), which is called PSGD, is equivalent to the GD in the warped weight space, a space made by warping the original weight space via an appropriately designed invertible function... (read more)

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