Summary of On the Vc Dimension Of Deep Group Convolutional Neural Networks, by Anna Sepliarskaia et al.
On the VC dimension of deep group convolutional neural networks
by Anna Sepliarskaia, Sophie Langer, Johannes Schmidt-Hieber
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistics Theory (math.ST); Machine Learning (stat.ML)
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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 GCNNs with ReLU activation function are studied for their generalization capabilities by deriving upper and lower bounds for their Vapnik-Chervonenkis (VC) dimension. The analysis explores how factors such as number of layers, weights, and input dimension affect the VC dimension, and compares these results to those known for other types of neural networks. Findings extend previous results on continuous GCNNs with two layers, providing new insights into the generalization properties of GCNNs and their dependence on input resolution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We learn about how Group Convolutional Neural Networks (GCNNs) can be good at learning from small samples by understanding the Vapnik-Chervonenkis (VC) dimension. This dimension tells us something about how well a model will work with new, unseen data. We find out that certain factors like the number of layers or input size affect this dimension and how it compares to other types of neural networks. |
Keywords
» Artificial intelligence » Generalization » Relu
