Sparse Autoencoders (SAEs) have recently gained attention as a means to improve the interpretability and steerability of Large Language Models (LLMs), both of which are essential for AI safety. In ...
One thing that continues to annoy me is that if I say: I get an error. I'm sure that there are worlds where this makes sense, but why oh why make me spend cycles ...
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🧬 Extract SAE features from protein language models (PLMs) 📊 Analyze and interpret learned features through association with protein annotations 🎨 Visualize feature patterns and relationships 🤗 ...
Understanding the mechanism of how neural networks learn features from data is a fundamental problem in machine learning. Our work explicitly connects the mechanism of neural feature learning to a ...
Sparse coding aims to find a parsimonious representation of an example given an observation matrix or dictionary. In this regard, Orthogonal Matching Pursuit (OMP) provides an intuitive, simple and ...
Monitoring of industrial assets often relies on high-frequency (HF) signal measurements. One difficulty of dealing with such measurements in the industrial context is the conciliation between the high ...
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