77 citations · 368 across the 31 of their papers we have counts for
6 papers · 1 filter
Cross-Domain Sentiment Classification with In-Domain Contrastive Learning
Tian Li, Xiang Chen, Shanghang Zhang +2
Contrastive learning (CL) has been successful as a powerful representation learning method. In this paper, we propose a contrastive learning framework for cross-domain sentiment cl…
Cross-Domain Sentiment Classification with Contrastive Learning and Mutual Information Maximization
Tian Li, Xiang Chen, Shanghang Zhang +2
Contrastive learning (CL) has been successful as a powerful representation learning method. In this work we propose CLIM: Contrastive Learning with mutual Information Maximization,…
HAWQV3: Dyadic Neural Network Quantization
Zhewei Yao, Zhen Dong, Zhangcheng Zheng +8
Current low-precision quantization algorithms often have the hidden cost of conversion back and forth from floating point to quantized integer values. This hidden cost limits the l…
CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAs
Zhen Dong, Dequan Wang, Qijing Huang +6
Deploying deep learning models on embedded systems has been challenging due to limited computing resources. The majority of existing work focuses on accelerating image classificati…
Algorithm-hardware Co-design for Deformable Convolution
Qijing Huang, Dequan Wang, Yizhao Gao +5
FPGAs provide a flexible and efficient platform to accelerate rapidly-changing algorithms for computer vision. The majority of existing work focuses on accelerating image classific…
ZeroQ: A Novel Zero Shot Quantization Framework
Yaohui Cai, Zhewei Yao, Zhen Dong +3
Quantization is a promising approach for reducing the inference time and memory footprint of neural networks. However, most existing quantization methods require access to the orig…