InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders InterPLM is a toolkit for extracting, analyzing, and visualizing interpretable features from protein ...

Abstract: Automated classification of learner-generated text to identify behavior, emotion, and cognition indicators, collectively known as learning engagement classification (LEC), has received ...

Abstract: The fast expansion of deep learning models has led to an increasing need for well-annotated datasets, while traditional manual annotation cannot meet this requirement. Current research on ...