Paper:
We present a provable, sampling-based approach for generating compact Convolutional Neural Networks (CNNs) by identifying and removing redundant filters from an over-parameterized network. Our algorithm uses a small batch of input data points to assign a saliency score to each filter and constructs an importance sampling distribution where filters that highly affect the output are sampled with correspondingly high probability. In contrast to existing filter pruning approaches, our method is simultaneously data-informed, exhibits provable guarantees on the size and performance of the pruned network, and is widely applicable to varying network architectures and data sets. Our analytical bounds bridge the notions of compressibility and importance of network structures, which gives rise to a fully-automated procedure for identifying and preserving filters in layers that are essential to the network's performance. Our experimental evaluations on popular architectures and data sets show that our algorithm consistently generates sparser and more efficient models than those constructed by existing filter pruning approaches.

Review:
Summary:
 
In this paper, the author propose a provable pruning method, and also provide a bound for the final pruning error.  Among most heuristics prune method,  pruning with mathematics guarantee is indeed more convincing. We expect this work can help people devoting some effort into more solid theoretical study in understanding the over-parameterized training.
Intuitively speaking, the sensitive neuron has greater contribution for the final output, reusing the corresponding filter and carefully rescale its value require many empirically attempts. To achieve a more reasonable algorithm, author prune the redundant channel by controlling the deviation of the summation statistically small,  and reusing the filter by important sampling the given channel. Experiment show that this method can reach a competitive prune radio against other pruning algorithm, and show robustly in retained parameters vs error experiment.
Weakness:
1. experiment is too weak
ImageNet model has great impact on most CV problem, and the current release models are flooding in the open source world. Author should at least provide a imagenet model and make this work more convincing. Besides, Author should also consider an experiment in modern lightweight network, vgg and resnet like model are out of fashion and so big that any one can make a sound result on it. 
2. lack of a comparing experiment for random select the top-k norm. 
Important sampling require an input of probability [p1, p2, p3, ... pn],  if those probabilities are nearly uniform, important sampling will behave like a random sampling method. In most case, if we want to prune the large channel network,  picking the top-1 significant filter or random sampling top-k filter will almost do the same thing.  
3. lack of further theory consideration
author only consider the single layer reconstruction, without discussing the overall accumulative error. Unlike the other deterministic method, sampling skill suffer variance propagation problem, the pre-layer variance will affect the sampling probability of next layer, how this pruning work if we change status of the pre-layer,  I didn't find any theoretical guarantee and only find a proof of single layer reconstruction bound.