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Article analysis:

The article Accelerating Convolutional Networks via Global & Dynamic Filter Pruning is a comprehensive review of various methods for compressing deep convolutional networks in order to speed up their performance. The article provides an overview of different approaches such as vector quantization, network surgery, huffman coding, group-wise brain damage, filter level pruning, global error reconstruction, eliminating spatial & channel redundancy and direct sparse convolutions for efficient inference.

The article is well researched and provides a detailed overview of the different approaches used to compress deep convolutional networks. The authors have provided citations from relevant research papers which adds credibility to the claims made in the article. Furthermore, the authors have provided a comprehensive list of references at the end of the article which further strengthens its trustworthiness and reliability.

However, there are some potential biases that should be noted when reading this article. For example, some of the approaches discussed may not be applicable to all types of deep convolutional networks or may not be suitable for certain applications due to their complexity or cost implications. Additionally, some approaches may require additional resources or expertise that may not be available in all cases. Therefore it is important to consider these factors when evaluating which approach is most suitable for a particular application or situation.

In conclusion, this article provides an informative overview of various methods used to compress deep convolutional networks in order to speed up their performance. However it is important to consider potential biases when evaluating which approach is most suitable for a particular application or situation due to its complexity or cost implications as well as any additional resources or expertise required by certain approaches.