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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Layer attack unlearning is a fast, accurate machine unlearning paradigm that operates at the layer level using attack and knowledge distillation.
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The paper shows how to induce large language models to behave like world models by teaching them precondition and effect knowledge about actions.
Thomas Gruber
Gruber treats formal ontologies as designed artifacts and proposes design criteria for building reusable, shareable knowledge specifications.
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Proposes structured knowledge distillation for semantic segmentation, distilling knowledge per pixel to improve dense prediction.
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MoMKD introduces a momentum memory mechanism for knowledge distillation to improve model compression in computational pathology.
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This paper introduces comparative knowledge distillation, a method that reduces the number of teacher calls needed for effective knowledge distillation.
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The paper introduces Student-friendly Knowledge Distillation (SKD), a method designed to be compatible with existing knowledge distillation techniques.
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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.
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The paper introduces correlation congruence, a knowledge distillation method that transfers correlation patterns between teacher and student networks to improve model compression.
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A review of knowledge distillation and student-teacher learning for visual intelligence, summarizing methods, applications, and future directions.
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Proposes GRACEs, a principled framework for selecting optimal teacher language models to improve knowledge distillation into smaller student models.
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SelecTKD introduces selective token-weighted knowledge distillation for autoregressive LLMs, formalizing token-level divergences and weighting tokens by importance.