Summary of Enhancing Neural Network Interpretability with Feature-aligned Sparse Autoencoders, by Luke Marks et al.
Enhancing Neural Network Interpretability with Feature-Aligned Sparse Autoencoders
by Luke Marks, Alasdair Paren, David Krueger, Fazl Barez
First submitted to arxiv on: 2 Nov 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 In this research paper, the authors propose a new regularization technique called Mutual Feature Regularization (MFR) to improve the feature learning capabilities of Sparse Autoencoders (SAEs). MFR encourages multiple SAEs trained in parallel to learn similar features that are more likely to correlate with input data. The authors demonstrate the effectiveness of MFR by training SAEs on synthetic data and comparing the learned features with known input features. They then scale MFR to real-world applications, including denoising electroencephalography (EEG) data and reconstructing GPT-2 Small activations. The results show that MFR can improve reconstruction loss by up to 21.21% on GPT-2 Small and 6.67% on EEG data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make artificial neural networks better at understanding what they’re seeing or hearing. Right now, these networks are good at doing certain tasks, but it’s hard for us to understand why they made certain decisions. The researchers came up with an idea called Mutual Feature Regularization that helps these networks learn more useful features by working together and learning similar things. They tested this idea on some fake data and then used it on real-world data like brain waves and language patterns. The results showed that this new technique can make the networks work better. |
Keywords
» Artificial intelligence » Gpt » Regularization » Synthetic data
