Batch normalization amplifies memorization of outlier samples in deep neural networks, directly increasing susceptibility to membership inference attacks.
org/abs/2310.130618
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
HEF frames emergence as a phase transition in mechanism landscapes with proofs of convergence under structural assumptions, empirically tested via grokking in transformers showing weight-norm peaks and accuracy collapse to ~0.9745.
citing papers explorer
-
Batch Normalization Amplifies Memorization and Privacy Risks
Batch normalization amplifies memorization of outlier samples in deep neural networks, directly increasing susceptibility to membership inference attacks.
-
Emergence via Phase Transitions: Mechanism Landscapes and Universal Convergence Across Complex Systems
HEF frames emergence as a phase transition in mechanism landscapes with proofs of convergence under structural assumptions, empirically tested via grokking in transformers showing weight-norm peaks and accuracy collapse to ~0.9745.