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 🤗 ...

Texture recognition underpins critical applications in industrial quality control, robotic manipulation, and biomedical imaging. Traditional deep dictionary learning methods for texture recognition ...

If you've interacted with artificial intelligence—whether through generative tools like ChatGPT or autonomous agents—you've witnessed a technology reshaping industries. Behind the scenes of this ...

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 ...

Python’s convenience and versatility mean that it’s used to build software in nearly every walk of IT life. One major niche is web services, where Python’s speed of development and flexible metaphors ...

With the advancements in wireless devices and mobile technology, there's increasing demand for people with digital image processing skills in order to extract useful information from the ever-growing ...

State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and ...