no code implementations • WS 2020 • Junxuan Chen, Xiang Li, Jiarui Zhang, Chulun Zhou, Jianwei Cui, Bin Wang, Jinsong Su
Finally, we combine the discourse structure information with the word embedding before it is fed into the encoder.
2 code implementations • 19 Apr 2019 • Wenjia Wang, Junxuan Chen, Jie Zhao, Ying Chi, Xuansong Xie, Li Zhang, Xian-Sheng Hua
The proposed model is trained and evaluated on a few publicly available datasets and has achieved the state-of-the-art accuracy with a mean Dice coefficient index of 0. 947 $\pm$ 0. 044.
no code implementations • CVPR 2017 • Zihao Hu, Junxuan Chen, Hongtao Lu, Tongzhen Zhang
To address this problem, we present a novel fully Bayesian treatment for supervised hashing problem, named Bayesian Supervised Hashing (BSH), in which hyperparameters are automatically tuned during optimization.
no code implementations • 20 Sep 2016 • Junxuan Chen, Baigui Sun, Hao Li, Hongtao Lu, Xian-Sheng Hua
Click through rate (CTR) prediction of image ads is the core task of online display advertising systems, and logistic regression (LR) has been frequently applied as the prediction model.
no code implementations • 13 Nov 2015 • Yaoyi Li, Junxuan Chen, Hongtao Lu
In this paper, we propose a novel method, dubbed Adaptive Affinity Matrix (AdaAM), to learn an adaptive affinity matrix and derive a distance metric from the affinity.