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Normal Alignment is presented, a novel statistical sign-recovery approach for S1 DNNs that infers neuron signs via normal-signature alignment and delivers higher voting accuracy and pushes erroneous predictions to low-confidence ranks, which further enables a more efficient combined method, eSOE + Alignment, by combining Normal Alignment with the hard-label SOE extension.
A novel algebraic approach to model extraction cuts clustering time by over 90%, enabling efficient attacks on hard-label max-pooling CNNs.
