Summary of Generalization Of Scaled Deep Resnets in the Mean-field Regime, by Yihang Chen et al.
Generalization of Scaled Deep ResNets in the Mean-Field Regime
by Yihang Chen, Fanghui Liu, Yiping Lu, Grigorios G. Chrysos, Volkan Cevher
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Despite the success of ResNet, its generalization properties are rarely explored beyond the lazy training regime. This work investigates scaled ResNets in the limit of infinitely deep and wide neural networks, where the gradient flow is described by a partial differential equation (PDE) in the mean-field regime. To derive generalization bounds, our analysis shifts from conventional Gram matrices to time-variant, distribution-dependent versions. We provide a global lower bound on the minimum eigenvalue of the Gram matrix under the mean-field regime and establish linear convergence of empirical error and upper bound of Kullback-Leibler (KL) divergence over parameters distributions. Our results offer new insights into deep ResNet generalization beyond lazy training regimes and contribute to understanding fundamental properties of deep neural networks. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a type of artificial intelligence called ResNets. They’re very good at learning, but nobody really knows how they work when they get really big. The researchers in this paper looked at what happens when you make these ResNets huge and study how well they can learn from examples. They found some new ways to understand how these networks work, which could help us build even better AI in the future. |
Keywords
* Artificial intelligence * Generalization * Resnet