A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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Investigates neural scaling laws for the ERM minimizer in the teacher-student setting beyond the random features regime, focusing on feature learning.
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The paper quantitatively analyzes mutual information curves of teacher-student pairs to explore the role of mutual information in knowledge distillation.
Feng Xiong, Leyan Xue, Hongyu Lin
Introduces Perception-Correction Distillation (PCD), a label-free method that uses downstream failure and teacher-student disagreement as complementary witnesses to correct perception failures in multimodal reasoners.
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A convolution-free vision transformer using a teacher-student strategy with attention-based distillation tokens.