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A Nuclear-Norm Lower Bound for Dithered Scalar Quantization of Matrix Products

Sao, Piyush et al.
arXiv (All) · Papers
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f-2-1g-1-3i-2-6information-theorymachine-learningnumerical-analysis
We consider the problem of minimizing error in quantized matrix multiplication $C=AB$. Scalar quantization of the factors introduces rounding errors whose scale depends on the maximum absolute entries -- the ranges -- of their rows and columns. These ranges determine the quantization grid steps. To
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