Summary of Self-normalized Resets For Plasticity in Continual Learning, by Vivek F. Farias et al.
Self-Normalized Resets for Plasticity in Continual Learning
by Vivek F. Farias, Adam D. Jozefiak
First submitted to arxiv on: 26 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Self-Normalized Resets (SNR) is a novel algorithm designed to mitigate plasticity loss in neural networks. Plasticity loss refers to the diminished ability of a network to adapt to new tasks when trained on a sequence of changing tasks. SNR resets neuron weights when evidence suggests their firing rate has dropped to zero, thereby enhancing adaptation performance. Across various continual learning problems and architectures, SNR outperforms competitor algorithms, showcasing robustness to its sole hyperparameter, the rejection percentile threshold. A theoretical investigation reveals that even adversarially initialized SNR can learn a single ReLU, whereas regularization-based approaches may fail to do so. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine training a neural network to do different tasks one after another. As you train it more and more, it starts to forget how to adapt to new tasks. This is called “plasticity loss.” Researchers developed an algorithm called Self-Normalized Resets (SNR) that helps the network remember how to adapt better. They tested SNR on various problems and showed that it works better than other algorithms. The team also found that SNR can learn to recognize certain patterns even when started with incorrect information. |
Keywords
» Artificial intelligence » Continual learning » Hyperparameter » Neural network » Regularization » Relu
