arXiv:2603.04198v2 Announce Type: replace-cross Abstract: Sparse autoencoders (SAEs) are widely used to extract human-interpretable features from neural network activations, but their learned features can vary substantially across random seeds and training choices. To improve stability, we studied weight regularization by adding L1 or L2 penalties on encoder and decoder weights, and evaluate how regularization interacts with common SAE training defaults. On MNIST, we observe that L2 weight regularization produces a core of highly aligned features and, when combined with tied initialization and
The research addresses known issues with stability and interpretability in Sparse Autoencoders (SAEs), a key component in understanding and improving neural networks.
Improved stability and steerability of SAEs will enhance the reliability and interpretability of AI models, making them more trustworthy and efficient for downstream applications.
This research provides a methodology to create more predictable and understandable sparse representations within AI models, potentially accelerating AI development and deployment in sensitive areas.
- · AI researchers
- · Machine learning engineers
- · Industries relying on interpretable AI
- · Developers of unstable AI models
More stable and interpretable AI features will lead to faster debugging and development cycles for complex AI systems.
Increased trust in AI explanations could accelerate the adoption of AI in regulated industries, where transparency is critical for ethical and safety concerns.
Standardisation of SAE training practices could emerge, fostering better collaboration and reproducibility across the AI research community.
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Read at arXiv cs.LG