Summary of On How Iterative Magnitude Pruning Discovers Local Receptive Fields in Fully Connected Neural Networks, by William T. Redman et al.
On How Iterative Magnitude Pruning Discovers Local Receptive Fields in Fully Connected Neural Networks
by William T. Redman, Zhangyang Wang, Alessandro Ingrosso, Sebastian Goldt
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Iterative magnitude pruning (IMP) has become a popular method for extracting sparse subnetworks that can be trained to high performance. However, the mechanism driving its success remains unclear. One possibility is that IMP extracts subnetworks with good inductive biases, which facilitate performance. Recent work showed that applying IMP to fully connected neural networks (FCNs) leads to the emergence of local receptive fields (RFs), a feature found in mammalian visual cortex and convolutional neural networks. This study hypothesizes that IMP iteratively increases the non-Gaussian statistics of FCN representations, creating a feedback loop that enhances localization. Here, we demonstrate that non-Gaussian input statistics are necessary for IMP to discover localized RFs. We also develop a new method, the “cavity method,” to measure the effect of individual weights on the statistics of FCN representations, showing that IMP increases the non-Gaussianity of pre-activations, leading to the formation of localized RFs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about a way to make computer models (like those used in self-driving cars or medical diagnosis) better. The method is called iterative magnitude pruning (IMP). IMP helps create “sparse” subnetworks that can be trained quickly and accurately. But researchers didn’t understand why it worked so well. One idea is that IMP creates a kind of “filter” in the computer model, making it more like how our brains process images. This study tested this idea by looking at what happens when we use IMP on simple neural networks. The results show that IMP does create a filter-like effect, which makes the computer models better. |
Keywords
» Artificial intelligence » Pruning
