A quantization vector derived from a donor model via weight-space arithmetic can be added to a receiver model to improve post-PTQ Top-1 accuracy by up to 60 points in 3-bit settings without receiver-side QAT or data.
Sharpness-aware quantization for deep neural networks
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Learning quantization-aware linear paths in weight space yields a midpoint whose direct quantization matches quantization-aware training performance without using straight-through estimators.
Directional sharpness is introduced as a metric that correlates more strongly with generalization, identifies poor generalization more reliably, and supports efficient auditing and zero-knowledge certification.
citing papers explorer
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Zero-Shot Quantization via Weight-Space Arithmetic
A quantization vector derived from a donor model via weight-space arithmetic can be added to a receiver model to improve post-PTQ Top-1 accuracy by up to 60 points in 3-bit settings without receiver-side QAT or data.
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Neural Network Quantization by Learning Low-Loss Subspaces
Learning quantization-aware linear paths in weight space yields a midpoint whose direct quantization matches quantization-aware training performance without using straight-through estimators.
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Certification of Machine Learning Models via Directional Sharpness
Directional sharpness is introduced as a metric that correlates more strongly with generalization, identifies poor generalization more reliably, and supports efficient auditing and zero-knowledge certification.