402 citations
- Alibaba Group (China)CN60 papers
- Zhejiang UniversityCN46 papers
- Bellevue Hospital CenterUS30 papers
- Peking UniversityCN22 papers
- Nanyang Technological UniversitySG21 papers
- Tsinghua UniversityCN15 papers
- Chinese Academy of SciencesCN14 papers
- Renmin University of ChinaCN13 papers
- Shanghai Jiao Tong UniversityCN11 papers
- Tianjin UniversityCN11 papers
- Alibaba Group (Cayman Islands)KY10 papers
- East China Normal UniversityCN10 papers
59 papers · 1 filter
HoMM: Higher-order Moment Matching for Unsupervised Domain Adaptation
Chao Chen, Zhihang Fu, Zhihong Chen +4
Minimizing the discrepancy of feature distributions between different domains is one of the most promising directions in unsupervised domain adaptation. From the perspective of dis…
Tracing the Propagation Path: A Flow Perspective of Representation Learning on Graphs
Menghan Wang, Kun Zhang, Gulin Li +2
Graph Convolutional Networks (GCNs) have gained significant developments in representation learning on graphs. However, current GCNs suffer from two common challenges: 1) GCNs are…
Acquiring Knowledge from Pre-trained Model to Neural Machine Translation
Rongxiang Weng, Heng Yu, Shujian Huang +2
Pre-training and fine-tuning have achieved great success in the natural language process field. The standard paradigm of exploiting them includes two steps: first, pre-training a m…
Amora: Black-box Adversarial Morphing Attack
Run Wang, Felix Juefei-Xu, Qing Guo +4
Nowadays, digital facial content manipulation has become ubiquitous and realistic with the success of generative adversarial networks (GANs), making face recognition (FR) systems s…
AdvKnn: Adversarial Attacks On K-Nearest Neighbor Classifiers With Approximate Gradients
Xiaodan Li, Yuefeng Chen, Yuan He +1
Deep neural networks have been shown to be vulnerable to adversarial examples---maliciously crafted examples that can trigger the target model to misbehave by adding imperceptible…
Improving Neural Relation Extraction with Positive and Unlabeled Learning
Zhengqiu He, Wenliang Chen, Yuyi Wang +3
We present a novel approach to improve the performance of distant supervision relation extraction with Positive and Unlabeled (PU) Learning. This approach first applies reinforceme…