Summary of Plastic Learning with Deep Fourier Features, by Alex Lewandowski et al.
Plastic Learning with Deep Fourier Features
by Alex Lewandowski, Dale Schuurmans, Marlos C. Machado
First submitted to arxiv on: 27 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper addresses the issue of deep neural networks struggling to learn continually in non-stationary environments, known as loss of plasticity. The authors identify underlying principles that lead to plastic algorithms, showing that linear function approximation and deep linear networks do not suffer from this problem. They propose deep Fourier features, a combination of sine and cosine functions in every layer, which balances linearity and nonlinearity to provide high trainability over the course of learning. Empirical results demonstrate significant improvements in continual learning performance by replacing ReLU activations with deep Fourier features, across various datasets including CIFAR10, CIFAR100, and tiny-ImageNet. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps solve a problem where artificial intelligence models have trouble learning new things when the information they’re learning is changing. The authors find some key principles that make it easier for AI to learn continually without getting stuck. They also propose a special way of combining simple and complex ideas in neural networks, which makes them much better at learning new things over time. This approach works well on many different datasets and can be used for various types of machine learning tasks. |
Keywords
» Artificial intelligence » Continual learning » Machine learning » Relu
