Summary of Stochastic Kernel Regularisation Improves Generalisation in Deep Kernel Machines, by Edward Milsom et al.
Stochastic Kernel Regularisation Improves Generalisation in Deep Kernel Machines
by Edward Milsom, Ben Anson, Laurence Aitchison
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 The paper introduces several modifications to convolutional deep kernel machines, a type of kernel-based machine learning model, to improve their generalization capabilities. The authors draw inspiration from ResNet-inspired architectures and achieve state-of-the-art (SOTA) performance on CIFAR-10 with 92.7% test accuracy. However, this still lags behind neural networks which can achieve over 94% test accuracy with similar architectures. To bridge the gap, the authors propose stochastic kernel regularization, adding noise to learned Gram matrices during training, and demonstrate a significant improvement in test accuracy, reaching 94.5%. This breakthrough has important theoretical and practical implications, showing that deep kernel methods can rival neural networks’ performance on complex tasks like image classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper makes a big discovery! It shows that a type of machine learning model called convolutional deep kernel machines can be just as good at recognizing pictures as super-powerful neural networks. To get to this point, the researchers made some clever changes to their model, adding “noise” to how it learns from data. This helped the model do much better on a test set of images. What’s cool is that this means we don’t need just one type of model (neural networks) to solve complex problems like image recognition – other approaches can work just as well. |
Keywords
» Artificial intelligence » Generalization » Image classification » Machine learning » Regularization » Resnet
