Search Results for author: Zhenyu Huang

Found 8 papers, 5 papers with code

Robust Domain Adaptation for Machine Reading Comprehension

no code implementations23 Sep 2022 Liang Jiang, Zhenyu Huang, Jia Liu, Zujie Wen, Xi Peng

Such a process will inevitably introduce mismatched pairs (i. e., noisy correspondence) due to i) the unavailable QA pairs in target documents, and ii) the domain shift during applying the QA construction model to the target domain.

Domain Adaptation Machine Reading Comprehension

Learning with Noisy Correspondence for Cross-modal Matching

1 code implementation NeurIPS 2021 Zhenyu Huang, guocheng niu, Xiao Liu, Wenbiao Ding, Xinyan Xiao, Hua Wu, Xi Peng

Based on this observation, we reveal and study a latent and challenging direction in cross-modal matching, named noisy correspondence, which could be regarded as a new paradigm of noisy labels.

Image-text matching Memorization +2

Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network

1 code implementation19 Apr 2021 Hongzuo Xu, Yijie Wang, Songlei Jian, Zhenyu Huang, Ning Liu, Yongjun Wang, Fei Li

We obtain an optimal attention-guided embedding space with expanded high-level information and rich semantics, and thus outlying behaviors of the queried outlier can be better unfolded.

Anomaly Detection Outlier Interpretation

Partially View-aligned Representation Learning with Noise-robust Contrastive Loss

1 code implementation CVPR 2021 Mouxing Yang, Yunfan Li, Zhenyu Huang, Zitao Liu, Peng Hu, Xi Peng

To solve such a less-touched problem without the help of labels, we propose simultaneously learning representation and aligning data using a noise-robust contrastive loss.

Clustering Contrastive Learning +2

Partially View-aligned Clustering

no code implementations NeurIPS 2020 Zhenyu Huang, Peng Hu, Joey Tianyi Zhou, Jiancheng Lv, Xi Peng

To solve this practical and challenging problem, we propose a novel multi-view clustering method termed partially view-aligned clustering (PVC).


Adaptive Power System Emergency Control using Deep Reinforcement Learning

1 code implementation9 Mar 2019 Qiuhua Huang, Renke Huang, Weituo Hao, Jie Tan, Rui Fan, Zhenyu Huang

Power system emergency control is generally regarded as the last safety net for grid security and resiliency.

Benchmarking reinforcement-learning +1

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