A2G-DiffRec applies adaptive autoguidance in diffusion recommenders, learning to balance main and weak model outputs via fairness-aware regularization to improve item exposure fairness with only marginal accuracy loss.
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An optimization-based deep learning pipeline selects informative patches from H&E whole-slide images to classify breast cancer into PAM50 subtypes, achieving F1 scores of 0.88 internally and 0.80 externally.
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Adaptive Autoguidance for Item-Side Fairness in Diffusion Recommender Systems
A2G-DiffRec applies adaptive autoguidance in diffusion recommenders, learning to balance main and weak model outputs via fairness-aware regularization to improve item exposure fairness with only marginal accuracy loss.
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A deep learning pipeline for PAM50 subtype classification using histopathology images and multi-objective patch selection
An optimization-based deep learning pipeline selects informative patches from H&E whole-slide images to classify breast cancer into PAM50 subtypes, achieving F1 scores of 0.88 internally and 0.80 externally.