黃高，清華大學助理教授、博士生導師。籍貫道林鎮華鑫市村，2005年畢業于寧鄉市第一高級中學，27歲獲得清華大學博士學位，隨后前往美國康奈爾大學計算機系繼續博士后深造，博士后第二年他的研究《Densely Connected Convolutional Networks》就獲cvpr2017最佳論文，并被編入多本深度學習著作，單篇引用量已經接近1.1萬次。
曾獲2016年全國百篇重大影響國際學術論文、2018年世界人工智能大會Super AI Leader（SAIL）先鋒獎、2019年吳文俊人工智能優秀青年獎等多項榮譽。如今，年僅33歲的黃高已是清華大學自動化系助理教授，博士生導師。
2020年9月9日，獲得2020年阿里巴巴達摩院青橙獎。.
教育背景
2005年9月至2009年7月 北京航空航天大學自動化學院，獲學士
2009年9月至2015年7月 清華大學自動化系，獲博士學位
工作履歷
2018年12月至今 清華大學自動化系 助理教授
學術兼職
AAAI Senior Program Committee Member（2018，2020）
擔任NeurIPS, ICML,CVPR, ICCV, ECCV, ICLR, AAAI等國際學術會議和JMLR, TPAMI, TIP, TNNLS等國際期刊審稿人
研究領域
研究概況
1. 基于遙感數據的智能地物分類與目標檢測方法，國家中華人民共和國國家自然科學基金委員會，2020.01-2022.12，項目負責人
2. 基于跨媒體知識圖譜的因果計算，國家科技部，2020.01-2022.12，課題骨干
3. 面向深度學習的自適應推理方法研究，北京智源人工智能研究院，2019.06-2020.05，項目負責人
4. 基于云仿真的復雜產品控制系統智能設計方法，北京電子工程總體研究所，2019.07-2021.06，項目負責人
5. 視覺驅動的深度強化學習算法及其在游戲智能導航AI中的應用，騰訊控股，2019.03-2020.03，項目負責人
獎勵與榮譽
2021年 入選《麻省理工科技評論》亞太地區“35歲以下科技創新35人”榜單
2018年 世界人工智能大會Super AI Leader（SAIL）先鋒獎
2017年 cvpr最佳論文獎
2016年 全國百篇最具影響國際學術論文
2015年 清華大學優秀畢業生
學術成果
主要會議論文
1. Shuang Li, Chi Harold Liu, Qiuxia Lin, Binhui Xie, Zhengming Ding, Gao Huang, Jian Tang. Domain Conditioned 適應 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, 溫哥華, Canada.
3. Yulin Wang*, Xuran pan*, Shiji Song, Hong Zhang, Cheng Wu, Gao Huang. Implicit Semantic 數據 Augmentation for Deep Networks, Neural Information Processing Systems (NeurIPS) 2019, 溫哥華, 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, 溫哥華, Canada.
5. Hao Li, Hong Zhang, Xiaojuan Qi, Ruigang Yang, Gao Huang. Improved Techniques for Training Adaptive Deep Networks, International Conference on Computer 異象 (ICCV) 2019, 首爾特別市, Korea.
6. Shuang Li, Chi Harold Liu, Binhui Xie, Limin Su, Zhengming Ding, Gao Huang. Joint Adversarial Domain 適應, 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 意象 Depth Estimation on Mobile Devices, International Conference on Robotics and 自動化技術 (ICRA), 2019, 蒙特利爾, Canada.
8. 壯族 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 棱錐 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 基團 Convolutions. IEEE Conference on 計算機 異象 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. 資源 Aware Person Re-identification across 倍數 Resolutions. IEEE Conference on 計算機 異象 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 資源 Efficient 意象 Classification. International Conference on Learning Representations (ICLR Oral), 2018, 溫哥華, Canada.
13. 壯族 Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan and Changshui Zhang. Learning Efficient ConvNets through Network Slimming. International Conference on 計算機 異象 (ICCV), 2017, Venice, Italy.
14. Gao Huang*, 壯族 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, 壯族 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*, 壯族 Liu, Daniel Sedra and Kilian Weinberger. Deep networks with stochastic depth. European Conference on 計算機 異象 (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, 南阿拉巴馬大學
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. 量綱 還原 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 成姓 吳語 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 數據 Mining (KDD), 2014, New York, NY, USA.
24. Zhixiang Xu, Matt Kusner, Gao Huang and Kilian Weinberger. Anytime 表征 learning. International Conference on Machine Learning (ICML), 2013, Atlanta GA, USA.
主要期刊論文
1. Yulin Wang, Rui Huang, Gao Huang*, Shiji Song, Cheng 吳語 Collaborative learning with corrupted labels, Neural Networks, 125, pp. 205-213, 2020.
2. Gao Huang, 壯族 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 量綱 還原 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 ctrl 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 意象 Processing, 27(9), pp. 4260-4273, 2018.
8. Shuang Li, Shiji Song, Gao Huang, 成姓 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. 量綱 還原 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 吳語 Efficient lasso training from a geometrical 透視 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 吳語 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 吳語 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 向量 regression for uncertain input and output 數據, IEEE Transactions on Neural Networks and Learning System, 23 (11), pp. 1690-1700, 2012.
19. Gao Huang, Shiji Song and Cheng 吳語 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.
參考資料 >
35歲以下科技創新35人亞太榜單落地中國：20位來自中國_科技湃_.澎湃新聞.2021-10-28
10名青年科學家獲發千萬元獎金，鐘南山學生的學生獲獎_10%公司_澎湃新聞-The Paper.澎湃新聞網.2020-09-09