Summary of Deep Networks Always Grok and Here Is Why, by Ahmed Imtiaz Humayun et al.
Deep Networks Always Grok and Here is Why
by Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 As machine learning educators, we can summarize this research paper as follows: The phenomenon of grokking, where deep neural networks (DNNs) generalize long after achieving near-zero training error, has been observed in controlled settings. However, the study reveals that grokking is actually widespread and occurs in various practical scenarios, such as training convolutional neural networks on CIFAR10 or ResNets on Imagenette datasets. The researchers introduce the concept of delayed robustness, where DNNs become robust against adversarial examples long after interpolation and generalization. They provide an analytical explanation for the emergence of both delayed generalization and delayed robustness based on local complexity measures, which track the density of linear regions in a DNN’s input-output mapping. The study finds that these linear regions undergo a phase transition during training, resulting in grokking as a post-training phenomenon. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Grokking is a mysterious process where deep learning models become super smart long after they’ve finished learning. Researchers have seen this happen in certain situations before, but now they’ve discovered it’s way more common than we thought! They found that big models like convolutional neural networks and ResNets can start to understand things really well even after they’ve stopped training. This is important because it means these models might be able to handle tricky problems and unexpected situations in a more clever way. |
Keywords
* Artificial intelligence * Deep learning * Generalization * Machine learning
