Review for NeurIPS paper: Pruning neural networks without any data by iteratively conserving synaptic flow

Weaknesses: Method: - Theorem 1 is not new, and similar results were already presented in previous work [Liang et al., 2019]. For the proof of Theorem 1, In L171, there are no bias terms that appeared in computing z_j. However, for ResNet and VGGNet they all have bias terms at each layer, so this theorem does not apply for them. For example, when the input data is very sparse and most of its dimensions are not correlated with the prediction, then you can probably prune a lot at the input layer. Therefore, I am not convinced with the motivation of data-independent pruning.