Summary of A Margin-based Multiclass Generalization Bound Via Geometric Complexity, by Michael Munn et al.
A Margin-based Multiclass Generalization Bound via Geometric Complexity
by Michael Munn, Benoit Dherin, Javier Gonzalvo
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 A novel approach to understanding deep neural networks’ generalization capabilities is presented in this paper, which explores margin-based multiclass generalization bounds. By leveraging a recent complexity measure called geometric complexity, the authors derive a new upper bound on the generalization error that scales with the margin-normalized geometric complexity of the network. This bound holds for various data distributions and model classes. The authors empirically investigate their findings using a ResNet-18 model trained with SGD on CIFAR-10 and CIFAR-100 datasets, including original and random labels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists are trying to figure out how deep learning works really well. They’re looking at special math formulas that help us understand why neural networks can learn so much from a little data. The authors came up with a new way to measure how well a network generalizes using something called geometric complexity. They tested their idea on a type of network and some datasets, seeing if it works as expected. |
Keywords
* Artificial intelligence * Deep learning * Generalization * Resnet
