1. A significant weakness of this paper is the lack of clarity in explaining the implementation of the core concept, which involves the use of strictly diagonal matrices and the proposed Gradual Mask (GM). Figure 2 suggests that the GM matrix is element-wise multiplied by the matrix A, but the description implies a different interpretation, where it functions as a learning rate for each element in A. This discrepancy needs further clarification to provide a complete understanding of the method.
2. The hyper-parameters $b$ (bit-width) and $\alpha$ (stability factor) may introduce significant computational overhead in the pursuit of determining the optimal trade-off between model size and accuracy.
2. The hyper-parameters $b$ (bit-width) and $\alpha$ (stability factor) may introduce significant computational overhead in the pursuit of determining the optimal trade-off between model size and accuracy.