8 citations · 10 across the 2 of their papers we have counts for
3 papers
cs.LG2021★ 2 cited
A Layer-wise Adversarial-aware Quantization Optimization for Improving Robustness
Chang Song, Riya Ranjan, Hai Li
Neural networks are getting better accuracy with higher energy and computational cost. After quantization, the cost can be greatly saved, and the quantized models are more hardware…
cs.LG2021★ 8 cited
Improving Adversarial Robustness in Weight-quantized Neural Networks
Chang Song, Elias Fallon, Hai Li
Neural networks are getting deeper and more computation-intensive nowadays. Quantization is a useful technique in deploying neural networks on hardware platforms and saving computa…
cs.LG2019
Feedback Learning for Improving the Robustness of Neural Networks
Chang Song, Zuoguan Wang, Hai Li
Recent research studies revealed that neural networks are vulnerable to adversarial attacks. State-of-the-art defensive techniques add various adversarial examples in training to i…