Summary of Gradient Routing: Masking Gradients to Localize Computation in Neural Networks, by Alex Cloud et al.
Gradient Routing: Masking Gradients to Localize Computation in Neural Networks
by Alex Cloud, Jacob Goldman-Wetzler, Evžen Wybitul, Joseph Miller, Alexander Matt Turner
First submitted to arxiv on: 6 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 The paper introduces a novel training method called gradient routing, which isolates capabilities to specific subregions of a neural network. This technique applies data-dependent, weighted masks to gradients during backpropagation, allowing users to configure which parameters are updated by which data points. The authors demonstrate that gradient routing can be used for interpretable representation learning, robust unlearning, and scalable oversight of reinforcement learners. They show that the approach localizes capabilities even when applied to a limited subset of the data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to train neural networks called gradient routing. It’s like giving the network instructions on what to do with each piece of information it gets. This helps make sure the network doesn’t learn things it shouldn’t, and that it can forget things if needed. The authors show that this method works well for learning representations, forgetting old knowledge, and keeping track of what different parts of a network are doing. They also found that it can be used with limited data, which is important in many real-world applications. |
Keywords
* Artificial intelligence * Backpropagation * Neural network * Representation learning
