千赢国/qy88千嬴国际官网-手机版

师资队伍
黄高

助理教授

系统集成研究所

电话:
地点:北京千赢国系


教育背景


2005年9月至2009年7月 北京航空航天大学自动化学院,获学士学位

2009年9月至2015年7月 千赢国系,获博士学位


工作履历


2015年10月至2018年8月 美国康奈尔大学 博士后

2018年12月至今 千赢国系 助理教授


学术兼职


中国人工智能学会模式识别专委会委员

中国图象图形学学会机器视觉专委会委员

CVPR Area Chair(2021)

AAAI Senior Program Committee Member(2018,2020)

担任NeurIPS, ICML, CVPR, ICCV, ECCV, ICLR, AAAI等国际学术会议和JMLR, TPAMI, TIP, TNNLS等国际期刊审稿人


研究领域


机器学习、深度学习、计算机视觉、强化学习


研究概况


1. 基于遥感数据的智能地物分类与目标检测方法,国家自然科学基金委,2020.01-2022.12,项目负责人

2. 基于跨媒体知识图谱的因果计算,国家科技部,2020.01-2022.12,课题骨干

3. 面向深度学习的自适应推理方法研究,北京智源人工智能研究院,2019.06-2020.05,项目负责人

4. 基于云仿真的复杂种类控制系统智能设计方法,北京电子工程总体研究所,2019.07-2021.06,项目负责人

5. 视觉驱动的深度强化学习算法及其在游戏智能导航AI中的应用,腾讯集团,2019.03-2020.03,项目负责人


奖励与荣誉


2019年 吴文俊人工智能优秀青年奖

2018年 世界人工智能大会Super AI Leader(SAIL)先锋奖

2018年 中国人工智能学会自然科学一等奖

2017年 CVPR最佳论文奖

2016年 中国自动化学会优秀博士学位论文奖

2016年 全国百篇最具影响国际学术论文

2015年 清华大学优秀博士论文一等奖

2015年 清华大学优秀毕业生


学术成果


主要会议论文

1. Shuang Li, Chi Harold Liu, Qiuxia Lin, Binhui Xie, Zhengming Ding, Gao Huang, Jian Tang. Domain Conditioned Adaptation Network, AAAI Conference on Artificial Intelligence (AAAI), 2020, New York, USA.

2. Haowei He, Gao Huang, Yang Yuan. Asymmetric Valleys: Beyond Sharp and Flat Local Minima, Neural Information Processing Systems (NeurIPS Spotlight) 2019, Vancouver, Canada.

3. Yulin Wang*, Xuran Pan*, Shiji Song, Hong Zhang, Cheng Wu, Gao Huang. Implicit Semantic Data Augmentation for Deep Networks, Neural Information Processing Systems (NeurIPS) 2019, Vancouver, Canada.

4. Wenjie Shi, Shiji Song, Hui Wu, Ya-Chu Hsu, Cheng Wu, Gao Huang. Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning, Neural Information Processing Systems (NeurIPS) 2019, Vancouver, Canada.

5. Hao Li, Hong Zhang, Xiaojuan Qi, Ruigang Yang, Gao Huang. Improved Techniques for Training Adaptive Deep Networks, International Conference on Computer Vision (ICCV) 2019, Seoul, Korea.

6. Shuang Li, Chi Harold Liu, Binhui Xie, Limin Su, Zhengming Ding, Gao Huang. Joint Adversarial Domain Adaptation, ACM Multimedia (ACM MM) 2019, Nice, France.

7. Yan Wang*, Zihang Lai*, Gao Huang, Brian Wang, Laurens van der Maaten, Mark Campbell, Kilian Q. Weinberger. Anytime Stereo Image Depth Estimation on Mobile Devices, International Conference on Robotics and Automation (ICRA), 2019, Montreal, Canada.

8. Zhuang Liu*, Mingjie Sun*, and Tinghui, Zhou, Gao Huang, Trevor Darrell. Rethinking the value of network pruning, International Conference on Learning Representations (ICLR), 2019, New Orleans, USA.

9. Yang Fu, Yunchao Wei, Yuqian Zhou, Honghui Shi, Gao Huang, Xinchao Wang, Zhiqiang Yao, Thomas Huang, Horizontal Pyramid Matching for Person Re-identification, AAAI Conference on Artificial Intelligence (AAAI), 2019, Hawaii USA.

10. Gao Huang*, Shichen Liu*, Laurens van der Maaten and Kilian Weinberger. CondenseNet: An Efficient DenseNet using Learned Group Convolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, Salt Lake City, USA.

11. Yan Wang*, Lequn Wang*, Yurong You*, Xu Zou, Vincent Chen, Serena Li, Gao Huang, Bharath Hariharan, Kilian Weinberger. Resource Aware Person Re-identification across Multiple Resolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, Salt Lake City, USA.

12. Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten and Kilian Weinberger. Multi-Scale Dense Convolutional Networks for Resource Efficient Image Classification. International Conference on Learning Representations (ICLR Oral), 2018, Vancouver, Canada.

13. Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan and Changshui Zhang. Learning Efficient ConvNets through Network Slimming. International Conference on Computer Vision (ICCV), 2017, Venice, Italy.

14. Gao Huang*, Zhuang Liu*, Laurens van de Maaten and Kilian Weinberger. Densely Connected Convolutional Networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, Hawaii, USA. Oral presentation. (Best Paper Award)

15. Gao Huang*, Yixuan Li*, Geoff Pleiss, Zhuang Liu, John E. Hopcroft and Kilian Weinberger. Snapshot Ensembles: Train 1, Get M for Free. International Conference on Learning Representations (ICLR), 2017, Toulon, France.

16. Gao Huang*, Chuan Guo*, Matt Kusner, Yu Sun, Fei Sha and Kilian Weinberger. Supervised Word Mover’s Distance. Neural Information Processing Systems (NIPS), 2016, Barcelona, Spain. Oral presentation.

17. Gao Huang*, Yu Sun*, Zhuang Liu, Daniel Sedra and Kilian Weinberger. Deep networks with stochastic depth. European Conference on Computer Vision (ECCV), 2016, Amsterdam, Netherlands. Spotlight. (This paper was recommended as an Oral Presentation at NIPS 2016 Deep Learning Symposium.)

18. Gao Huang, Jianwen Zhang, Shiji Song and Zheng Chen. Maximin separation probability clustering. The AAAI Conference on Artificial Intelligence (AAAI), 2015, Austin, USA.

19. Yihe Wan, Shiji Song and Gao Huang. Incremental Extreme Learning Machine Based on Cascade Neural Networks. IEEE International Conference on Systems, Man and Cybernetics (IEEE SMC), 2015, Hong Kong.

20. Yanshang Gong, Shiji Song and Gao Huang. Dimension Reduction by Maximizing Pairwise Discriminations. IEEE International Conference on Systems, Man and Cybernetics (IEEE SMC). 2015, Hong Kong.

21. Chen Qin, Shiji Song and Gao Huang. Non-linear neighborhood component analysis based on constructive neural networks. IEEE International Conference on Systems, Man and Cybernetics (IEEE SMC), 2014, San Diego, CA, USA.

22. Gao Huang, Shiji Song, Zhixiang Xu, Kilian Weinberger and Cheng Wu. Transductive minimax probability machine. European Conference on Machine Learning (ECML), 2014, Nancy, France. Oral presentation.

23. Zhixiang Xu, Gao Huang, Kilian Weinberger, Alice Zheng. Gradient Boosted Feature Selection. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2014, New York, NY, USA.

24. Zhixiang Xu, Matt Kusner, Gao Huang and Kilian Weinberger. Anytime representation learning. International Conference on Machine Learning (ICML), 2013, Atlanta GA, USA.

主要期刊论文

1. Yulin Wang, Rui Huang, Gao Huang*, Shiji Song, Cheng Wu. Collaborative learning with corrupted labels, Neural Networks, 125, pp. 205-213, 2020.

2. Gao Huang, Zhuang Liu, Geoff Pleiss, Laurens van der Maaten and Kilian Weinberger. Convolutional Networks with Dense Connectivity, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2019 (In press).

3. Shuang Li, Chi Harold Liu and Gao Huang. Deep Residual Correction Network for Partial Domain Adaptation, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2019 (In Press).

4. Hangkai Hu, Shiji Song, Gao Huang. Self-Attention Based Temporary Curiosity in Reinforcement Learning Exploration, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019 (In Press).

5. Le Yang, Shiji Song, Shuang Li, Yiming Chen, Gao Huang. Graph Embedding-Base Dimension Reduction With Extreme Learning Machine, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019 (In press).

6. Benben Jiang, Zhifeng Guo, Qunxiong Zhu and Gao Huang. Dynamic minimax probability machine-based approach for fault diagnosis using pairwise discriminate analysis, IEEE Transactions on Control Systems Technology, 27(2), pp. 806-813, 2019.

7. Shuang Li, Shiji Song, Gao Huang, Zhengming Ding and Cheng Wu. Domain invariant and class discriminative feature learning for visual domain adaptation. IEEE Transactions on Image Processing, 27(9), pp. 4260-4273, 2018.

8. Shuang Li, Shiji Song, Gao Huang, Cheng Wu, “Cross-Domain Extreme Learning Machine for Domain Adaptation”, IEEE Transactions on Systems, Man, Cybernetics : Systems, 2018.

9. Yihe Wan, Shiji Song, Gao Huang, Shuang Li, Twin Extreme Learning Machine for Pattern Classification. Neurocomputing, 23(11): 1690-1700, 2017.

10. Shiji Song, Yanshang Gong, Yuli Zhang, Gao Huang and Guangbin Huang. Dimension Reduction by Minimum Error Minimax Probability Machine. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(1), pp. 58-69, 2016.

11. Shuang Li, Shiji Song and Gao Huang. Prediction reweighting for domain adaptation. IEEE Transactions on Neural Networks and Learning Systems, 2016.

12. Quan Zhou, Shiji Song, Gao Huang and Cheng Wu. Efficient lasso training from a geometrical perspective. Neurocomputing 168 (11), pp. 234-239, 2015.

13. Chen Qin, Shiji Song and Gao Huang and Lei Zhu. Unsupervised neighborhood component analysis for clustering. Neurocomputing, 168(11), pp. 609-617, 2015.

14. Gao Huang, Tianchi Liu, Yan Yang, Zhiping Lin, Shiji Song and Cheng Wu. Discriminative clustering via extreme learning machine, Neural Networks, 70(10), pp. 1-8, 2015.

15. Gao Huang, Guang-Bin Huang, Shiji Song and Keyou You. Trends in extreme learning machine: a review, Neural Networks, 61(2), pp. 32-48, 2015.

16. Gao Huang, Shiji Song, Jatinder Gupta and Cheng Wu. Semi-supervised and unsupervised extreme learning machines. IEEE Transactions on Cybernetics, 44 (12), pp. 2405-2417, 2014.

17. Gao Huang, Shiji Song, Jatinder Gupta and Cheng Wu. A second order cone programming approach for semi-supervised learning. Pattern Recognition, 46(12), pp. 3548-3558, 2013.

18. Gao Huang, Shiji Song, Cheng Wu and Keyou You. Robust support vector regression for uncertain input and output data, IEEE Transactions on Neural Networks and Learning System, 23 (11), pp. 1690-1700, 2012.

19. Gao Huang, Shiji Song and Cheng Wu. Orthogonal least squares algorithm for training cascade neural networks. IEEE Transactions on Circuits and Systems I: Regular Papers, 59 (11), pp. 2629-2637, 2012.

20. Quan Zhou, Shiji Song, Cheng Wu and Gao Huang. Kernelized LARS-LASSO for constructing radial basis function neural networks. Neural Computing and Applications, 23(7-8), pp. 1969-1976, 2013.


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