On five tabular security datasets at 10% labels, tuning only the classifier with Bayesian optimization recovers a median 86% of the gains from full joint SSL-classifier optimization.
Advances in neural information processing systems , volume=
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SNAC-Pack uses learned FPGA resource surrogates inside evolutionary neural architecture search to produce smaller, faster FPGA models for jet classification and qubit readout than BOP-based codesign.
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SemiScope: Disentangling Classifier Tuning and Joint Optimization in Semi-Supervised Security Classification
On five tabular security datasets at 10% labels, tuning only the classifier with Bayesian optimization recovers a median 86% of the gains from full joint SSL-classifier optimization.
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SNAC-Pack 2.0: Scaled-Out Surrogate Neural Architecture Codesign
SNAC-Pack uses learned FPGA resource surrogates inside evolutionary neural architecture search to produce smaller, faster FPGA models for jet classification and qubit readout than BOP-based codesign.