Summary of How Neural Networks Learn the Support Is An Implicit Regularization Effect Of Sgd, by Pierfrancesco Beneventano et al.
How Neural Networks Learn the Support is an Implicit Regularization Effect of SGD
by Pierfrancesco Beneventano, Andrea Pinto, Tomaso Poggio
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); 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 In this study, researchers explore how deep neural networks learn to identify the underlying structure of a target function. They discover that using mini-batch stochastic gradient descent (SGD) allows the model to effectively learn the relevant features in the first layer by shrinking irrelevant weights to zero. In contrast, vanilla gradient descent requires an explicit regularization term to achieve similar results. The team finds that this property of mini-batch SGD is due to a second-order implicit regularization effect proportional to the step size and batch size ratio. This study highlights the impact of optimization dynamics on feature learning and suggests that smaller batches can improve feature interpretability and reduce dependency on initialization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep neural networks are really good at identifying patterns in data. Researchers wanted to see how they learn what’s important. They found that when using a special kind of training, called mini-batch SGD, the network is great at figuring out which parts of the input matter most. This helps it get better at recognizing patterns and making accurate predictions. The study shows that this type of training has an extra benefit: it makes the features learned by the network easier to understand and less dependent on how the network is started. |
Keywords
* Artificial intelligence * Gradient descent * Optimization * Regularization * Stochastic gradient descent
