3 papers
cs.LG2026
A Qualitative Test-Risk Mechanism for Scaling Behavior in Normalized Residual Networks
Daning Cheng, Zeyu Liu, Jun Sun +4
The scaling behavior, in which test performance often improves as model size and data increase, is a central empirical phenomenon in modern deep learning, yet its theoretical basis…
cs.CR2025
Verification of Bit-Flip Attacks against Quantized Neural Networks
Yedi Zhang, Lei Huang, Pengfei Gao +3
In the rapidly evolving landscape of neural network security, the resilience of neural networks against bit-flip attacks (i.e., an attacker maliciously flips an extremely small amo…
cs.LG2024
Training Verification-Friendly Neural Networks via Neuron Behavior Consistency
Zongxin Liu, Zhe Zhao, Fu Song +4
Formal verification provides critical security assurances for neural networks, yet its practical application suffers from the long verification time. This work introduces a novel m…