This article explores how stochastic regularization in neural networks can improve performance on unseen categorical data, especially high-cardinality categorical features. It uses visualizations and SHAP values to understand how entity embeddings respond to this regularization technique.
How can an LLM be applied effectively for biomedical entity linking? Entity linking involves recognizing and extracting entities within the text and mapping them to standardized concepts in a large terminology. I
