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- Tsinghua UniversityCN130 papers
- Chinese University of Hong KongHK81 papers
- Chinese Academy of SciencesCN71 papers
- Peking UniversityCN68 papers
- Shanghai Jiao Tong UniversityCN58 papers
- Zhejiang UniversityCN54 papers
- University of Chinese Academy of SciencesCN52 papers
- Hong Kong University of Science and TechnologyHK46 papers
- Xiamen UniversityCN37 papers
- University of Hong KongHK34 papers
- Peng Cheng LaboratoryCN31 papers
- City University of Hong KongHK30 papers
26 papers · 2 filters
Fast Stochastic Ordinal Embedding with Variance Reduction and Adaptive Step Size
Ke Ma, Jinshan Zeng, Qianqian Xu +3
Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are based on semi…
Heterogeneous Graph-based Knowledge Transfer for Generalized Zero-shot Learning
Junjie Wang, Xiangfeng Wang, Bo Jin +3
Generalized zero-shot learning (GZSL) tackles the problem of learning to classify instances involving both seen classes and unseen ones. The key issue is how to effectively transfe…
Deep Minimax Probability Machine
Lirong He, Ziyi Guo, Kaizhu Huang +1
Deep neural networks enjoy a powerful representation and have proven effective in a number of applications. However, recent advances show that deep neural networks are vulnerable t…
Adaptive Activation Network and Functional Regularization for Efficient and Flexible Deep Multi-Task Learning
Yingru Liu, Xuewen Yang, Dongliang Xie +4
Multi-task learning (MTL) is a common paradigm that seeks to improve the generalization performance of task learning by training related tasks simultaneously. However, it is still…
Online Adaptive Asymmetric Active Learning with Limited Budgets
Yifan Zhang, Peilin Zhao, Shuaicheng Niu +4
Online Active Learning (OAL) aims to manage unlabeled datastream by selectively querying the label of data. OAL is applicable to many real-world problems, such as anomaly detection…
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement
Chao Yang, Xiaojian Ma, Wenbing Huang +4
This paper studies Learning from Observations (LfO) for imitation learning with access to state-only demonstrations. In contrast to Learning from Demonstration (LfD) that involves…