Summary of How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers, by Gon Buzaglo et al.
How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers
by Gon Buzaglo, Itamar Harel, Mor Shpigel Nacson, Alon Brutzkus, Nathan Srebro, Daniel Soudry
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This research paper investigates the generalization capabilities of over-parameterized Neural Networks (NNs) that interpolate training data. Typically, NNs are trained using Stochastic Gradient Descent (SGD) or its variants. However, recent studies have shown that randomly sampled NNs that perfectly classify training sets can also generalize well. The authors aim to understand this phenomenon and provide insights into the underlying mechanisms. By analyzing the properties of these random NN interpolators, the researchers demonstrate that they typically generalize well if there exists a narrow “teacher” NN that agrees with the labels. This is achieved through a rich prior over the NN functions, which is induced by the redundancy in the NN structure. This prior favors simpler functions that require fewer relevant parameters to represent, allowing learning to occur at a sample complexity proportional to the complexity of the teacher (roughly, the number of non-redundant parameters). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores why over-parameterized Neural Networks can generalize well when trained to zero loss. It shows that if there’s an underlying “teacher” NN that agrees with the labels, then a random NN interpolator will typically do just as well. The key finding is that this random NN has a prior over its functions that favors simpler ones, which are easier to learn. |
Keywords
* Artificial intelligence * Generalization * Stochastic gradient descent
