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Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation


Dec 22, 2020
Shuai Lin, Pan Zhou, Xiaodan Liang, Jianheng Tang, Ruihui Zhao, Ziliang Chen, Liang Lin

* Accepted by AAAI 2021 

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Learning Reinforced Agents with Counterfactual Simulation for Medical Automatic Diagnosis


Mar 14, 2020
Junfan Lin, Ziliang Chen, Xiaodan Liang, Keze Wang, Liang Lin

* Submitted to TPAMI 2020. In the experiments, our trained agent achieves the new state-of-the-art under various experimental settings and possesses the advantage of sample-efficiency and robustness compared to other existing MAD methods 

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Meta R-CNN : Towards General Solver for Instance-level Low-shot Learning


Sep 28, 2019
Xiaopeng Yan, Ziliang Chen, Anni Xu, Xiaoxi Wang, Xiaodan Liang, Liang Lin

* Published in ICCV-2019. Project: https://yanxp.github.io/metarcnn.html 

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Graph Neural Reasoning May Fail in Certifying Boolean Unsatisfiability


Sep 27, 2019
Ziliang Chen, Zhanfu Yang

* 6 pages 

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Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching


Jul 08, 2019
Ziliang Chen, Zhanfu Yang, Xiaoxi Wang, Xiaodan Liang, Xiaopeng Yan, Guanbin Li, Liang Lin

* ICML-19 

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Blending-target Domain Adaptation by Adversarial Meta-Adaptation Networks


Jul 08, 2019
Ziliang Chen, Jingyu Zhuang, Xiaodan Liang, Liang Lin

* CVPR-19 (oral). Code is available at http://github.com/zjy526223908/BTDA 

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Improved Hard Example Mining by Discovering Attribute-based Hard Person Identity


May 09, 2019
Xiao Wang, Ziliang Chen, Rui Yang, Bin Luo, Jin Tang


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Graph Neural Reasoning for 2-Quantified Boolean Formula Solvers


Apr 27, 2019
Zhanfu Yang, Fei Wang, Ziliang Chen, Guannan Wei, Tiark Rompf

* 5 Pages 

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FRAME Revisited: An Interpretation View Based on Particle Evolution


Jan 16, 2019
Xu Cai, Yang Wu, Guanbin Li, Ziliang Chen, Liang Lin


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Cost-effective Object Detection: Active Sample Mining with Switchable Selection Criteria


Sep 16, 2018
Keze Wang, Liang Lin, Xiaopeng Yan, Ziliang Chen, Dongyu Zhang, Lei Zhang

* Automatically determining whether an unlabeled sample should be manually annotated or pseudo-labeled via a novel self-learning process (Accepted by TNNLS 2018) The source code is available at http://kezewang.com/codes/ASM_ver1.zip 

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Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift


Mar 02, 2018
Ruijia Xu, Ziliang Chen, Wangmeng Zuo, Junjie Yan, Liang Lin

* Accepted for publication in Conference on Computer Vision and Pattern Recognition(CVPR), 2018 

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Deep Co-Space: Sample Mining Across Feature Transformation for Semi-Supervised Learning


Jul 28, 2017
Ziliang Chen, Keze Wang, Xiao Wang, Pai Peng, Ebroul Izquierdo, Liang Lin

* To appear in IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT), 2017 

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