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cs.LG2026
CutClean: Neural Network Pruning for Privacy-Preserving Inference
Leonardo Magliolo, Vito Paolo Pastore, Giuseppe Valenzise +1
Neural networks are increasingly deployed in high-stakes applications with growing privacy leakage concerns. We show that this privacy leakage can occur even in the absence of repr…
cs.LG2026
Learning High-Dimensional Parity Functions with Product Networks using Gradient Descent
Guillaume Larue, Louis-Adrien Dufrène, Quentin Lampin +2
Parity functions are fundamental Boolean operations with critical applications across machine learning, cryptography, and error correction. Yet, learning high-dimensional parity fu…