Summary of Generalizability Of Memorization Neural Networks, by Lijia Yu and Xiao-shan Gao and Lijun Zhang and Yibo Miao
Generalizability of Memorization Neural Networks
by Lijia Yu, Xiao-Shan Gao, Lijun Zhang, Yibo Miao
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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 The neural network memorization problem is studied to analyze the expressive power of neural networks for interpolating a finite dataset. Although memorization is believed to be related to strong generalizability in deep learning, there lacks theoretical study on generalizability of memorization neural networks. This paper provides the first theoretical analysis of this topic. It develops memorization and generalization theory under mild conditions on data distribution. The paper shows that memorization networks require a width at least equal to the data dimension to be generalizable, implying existing memorization networks are not generalizable. A lower bound is given for sample complexity of general memorization algorithms and exact sample complexity for constant-parameter memorization algorithms. It also shows that certain data distributions require memorization networks with exponential parameters in data dimension. Finally, an efficient and generalizable memorization algorithm is provided when the number of training samples exceeds efficient memorization sample complexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper studies how well neural networks can remember a dataset. Although people think this remembering helps deep learning models be good at new tasks, there’s no scientific study on why this remembering works. This paper does that research and finds some surprising results. It shows that to be good at new tasks, the networks need to have a certain number of “building blocks” (called layers) equal to the number of things in the data. The paper also gives a formula for how many training examples are needed for this remembering to work well. Finally, it provides a way to make these networks better and more useful. |
Keywords
» Artificial intelligence » Deep learning » Generalization » Neural network
