黄高 助理教授

系统集成研究所

地址:北京清华大学自动化系 邮编:100084
邮箱:gaohuang@tsinghua.edu.cn

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 清华大学自动化系,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 清华大学自动化系 助理教授

学术兼职

AAAI 2018 高级程序委员

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.节能型直线电磁压力机关键技术研发及产业化   科技部国家科技支撑计划 2012-2014   参与

2.基于数据驱动的风力发电机状态监控与故障诊断技术研究    国家教育部 2014.01-2016.12   参与

3.随机多级库存成本管理的风险建模与优化方法及其应用   自然科学基金委 2013.01-2016.12   参与

奖励与荣誉

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

2018年 吴文俊人工智能自然科学一等奖

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 全国百篇最具国际影响学术论文

学术成果

主要会议论文
1.Zhuang Liu*, Mingjie Sun*, Tinghui, Zhou, Gao Huang, Trevor Darrell. Rethinking the Value of Network Pruning, International Conference on Learning Representations (ICLR) 2019

2.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

3.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

4.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.

5.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), 2018, Vancouver, Canada. (Oral).

6.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; Best Paper Award)

7.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.

8.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).

9.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.

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

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

主要期刊论文
1.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, 2019.2.

2.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

3.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.

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

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

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

7.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.

8.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.

9.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.

10.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.

11.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.

12.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.