Derives non-asymptotic 2-norm and infinity-norm error bounds for deterministic and stochastic variants of OPTQ and Qronos PTQ algorithms.
Magr: Weight magnitude reduc- tion for enhancing post-training quantization
2 Pith papers cite this work. Polarity classification is still indexing.
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Permutation-COMQ is a new post-training quantization algorithm that reorders weights within layers and uses only dot-product and rounding steps to deliver the highest reported accuracy for 2-, 4-, and 8-bit medical foundation models.
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Provable Post-Training Quantization: Theoretical Analysis of OPTQ and Qronos
Derives non-asymptotic 2-norm and infinity-norm error bounds for deterministic and stochastic variants of OPTQ and Qronos PTQ algorithms.
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Weight Group-wise Post-Training Quantization for Medical Foundation Model
Permutation-COMQ is a new post-training quantization algorithm that reorders weights within layers and uses only dot-product and rounding steps to deliver the highest reported accuracy for 2-, 4-, and 8-bit medical foundation models.