Compositional interpretability defines explanations as commuting syntactic-semantic mapping pairs grounded in compositionality and minimum description length, with compressive refinement and a parsimony theorem guaranteeing concise human-aligned decompositions.
Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A slice-wise feature distillation framework for independent tensorization of neural network slices to achieve scalable compression with reduced fine-tuning costs.
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From Mechanistic to Compositional Interpretability
Compositional interpretability defines explanations as commuting syntactic-semantic mapping pairs grounded in compositionality and minimum description length, with compressive refinement and a parsimony theorem guaranteeing concise human-aligned decompositions.
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Fast Tensorization of Neural Networks via Slice-wise Feature Distillation
A slice-wise feature distillation framework for independent tensorization of neural network slices to achieve scalable compression with reduced fine-tuning costs.