Relational knowledge distillation
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Relational knowledge distillation extends traditional distillation by transferring structural relationships between data points from teacher to student.
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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Relational knowledge distillation extends traditional distillation by transferring structural relationships between data points from teacher to student.
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This survey systematically reviews knowledge distillation techniques for transferring capabilities from large proprietary LLMs to smaller models.
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This paper investigates the factors influencing the efficacy of knowledge distillation, a technique for compressing large models into smaller ones.
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This paper integrates knowledge distillation with self-supervised contrastive learning to improve student model performance without requiring labeled data.
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Proposes similarity-preserving knowledge distillation that uses pairwise similarity matrices as supervisory signals to train student networks.
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A comprehensive survey of knowledge distillation methods, covering recent techniques and categorizing approaches in the field.
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Proposes logit standardization as a pre-processing step to improve vanilla knowledge distillation performance.
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This paper proposes Teacher Assistant Knowledge Distillation (TAKD) to improve knowledge transfer by introducing an intermediate assistant model between teacher and student.
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A comprehensive survey of knowledge distillation advancements, covering foundational techniques, relation-based methods, and novel approaches.
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This survey comprehensively reviews knowledge distillation techniques for model compression and acceleration.
Torres H., Juan P.
Proposes a Competitive Mixture of Experts (CMoE) framework using Functional Language Logic to replace probabilistic token prediction with deterministic mathematical reasoning for paradox-free AI.