3 citations · 6 across the 2 of their papers we have counts for
2 papers
cs.LG2021★ 3 cited
DPNAS: Neural Architecture Search for Deep Learning with Differential Privacy
Anda Cheng, Jiaxing Wang, Xi Sheryl Zhang +3
Training deep neural networks (DNNs) for meaningful differential privacy (DP) guarantees severely degrades model utility. In this paper, we demonstrate that the architecture of DNN…
cs.CV2021★ 3 cited
Improving Binary Neural Networks through Fully Utilizing Latent Weights
Weixiang Xu, Qiang Chen, Xiangyu He +2
Binary Neural Networks (BNNs) rely on a real-valued auxiliary variable W to help binary training. However, pioneering binary works only use W to accumulate gradient updates during…