Quantization of neural classifiers produces measurable boundary shifts captured by Jaccard distances and flip rates that correlate between calibration and held-out sets across bit widths.
Moosavi-Dezfooli, A
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Boundary-Aware Quantization: Finite-Scale Decision Geometry of Neural Classifiers
Quantization of neural classifiers produces measurable boundary shifts captured by Jaccard distances and flip rates that correlate between calibration and held-out sets across bit widths.