17 citations · 42 across the 27 of their papers we have counts for
4 papers · 1 filter
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…
Towards Mixed-Precision Quantization of Neural Networks via Constrained Optimization
Weihan Chen, Peisong Wang, Jian Cheng
Quantization is a widely used technique to compress and accelerate deep neural networks. However, conventional quantization methods use the same bit-width for all (or most of) the…
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…
Generative Zero-shot Network Quantization
Xiangyu He, Qinghao Hu, Peisong Wang +1
Convolutional neural networks are able to learn realistic image priors from numerous training samples in low-level image generation and restoration. We show that, for high-level im…