Summary of Information-theoretic Generalization Bounds For Deep Neural Networks, by Haiyun He et al.
Information-Theoretic Generalization Bounds for Deep Neural Networks
by Haiyun He, Christina Lee Yu, Ziv Goldfeld
First submitted to arxiv on: 4 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT)
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 This paper investigates the role of depth in deep neural networks (DNNs) for supervised learning. The authors derive two hierarchical generalization bounds on the error rate using information-theoretic methods. The Kullback-Leibler divergence bound shows that deeper layers contribute to better generalization, while the 1-Wasserstein distance bound implies the existence of a “generalization funnel” layer. The study focuses on binary Gaussian classification with linear DNNs and analyzes the strong data processing inequality (SDPI) coefficient for three regularization methods: Dropout, DropConnect, and Gaussian noise injection. The results refine the generalization bounds to capture the contraction of information measures as network architecture parameters change. This work contributes to our understanding of how depth affects DNN performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how deep neural networks (DNNs) learn from data. The authors want to understand why DNNs can generalize well in real-world applications, even when they haven’t seen the exact same training data before. They develop two new mathematical formulas that show how deeper layers in a network help with generalization. The study also looks at how different ways of regularizing DNNs (like Dropout or Gaussian noise) affect their ability to generalize. Overall, this work helps us understand the importance of depth in deep neural networks and how it impacts their performance. |
Keywords
* Artificial intelligence * Classification * Dropout * Generalization * Regularization * Supervised
