22 citations · 43 across the 3 of their papers we have counts for
10 papers
Real World Robustness from Systematic Noise
Yan Wang, Yuhang Li, Ruihao Gong
Systematic error, which is not determined by chance, often refers to the inaccuracy (involving either the observation or measurement process) inherent to a system. In this paper, w…
A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration
Yuhang Li, Shikuang Deng, Xin Dong +2
Spiking Neural Network (SNN) has been recognized as one of the next generation of neural networks. Conventionally, SNN can be converted from a pre-trained ANN by only replacing the…
BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction
Yuhang Li, Ruihao Gong, Xu Tan +6
We study the challenging task of neural network quantization without end-to-end retraining, called Post-training Quantization (PTQ). PTQ usually requires a small subset of training…
Once Quantization-Aware Training: High Performance Extremely Low-bit Architecture Search
Mingzhu Shen, Feng Liang, Ruihao Gong +6
Quantization Neural Networks (QNN) have attracted a lot of attention due to their high efficiency. To enhance the quantization accuracy, prior works mainly focus on designing advan…
Binary Neural Networks: A Survey
Haotong Qin, Ruihao Gong, Xianglong Liu +3
The binary neural network, largely saving the storage and computation, serves as a promising technique for deploying deep models on resource-limited devices. However, the binarizat…
Efficient Bitwidth Search for Practical Mixed Precision Neural Network
Yuhang Li, Wei Wang, Haoli Bai +3
Network quantization has rapidly become one of the most widely used methods to compress and accelerate deep neural networks. Recent efforts propose to quantize weights and activati…