Summary of Effective Subset Selection Through the Lens Of Neural Network Pruning, by Noga Bar and Raja Giryes
Effective Subset Selection Through The Lens of Neural Network Pruning
by Noga Bar, Raja Giryes
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Deep learning models rely heavily on large amounts of annotated data, but annotating medical data, for instance, can be extremely costly. To address this issue, researchers have turned to subset selection, which involves choosing the most informative samples from a larger dataset. Interestingly, there is a connection between subset selection and neural network pruning, which aims to remove redundant connections in models. By applying insights from pruning to subset selection, a new approach has been developed that utilizes the norm criterion of neural network features to improve subset selection methods. In a series of experiments, this method demonstrated enhanced accuracy on various networks and datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning models need lots of labeled data to work well. However, labeling some types of data can be very expensive. One way to solve this problem is by choosing the most important pieces of data from a larger dataset. Did you know that there’s a connection between doing this and pruning neural networks? Pruning helps remove unnecessary parts of models. By using ideas from pruning, researchers have developed a new way to choose important data that works better than before. They tested it on different models and datasets and found that it improved their performance. |
Keywords
» Artificial intelligence » Deep learning » Neural network » Pruning
