基本介紹
- 中文名：鄧岳
- 國籍：中國
- 畢業院校：清華大學
- 學位/學歷：博士
- 職業：教師
- 職務：博士生導師，碩士生導師
- 主要成就：2020阿里巴巴達摩院青橙獎獲得者
- 教學職稱：教授
獲獎記錄,論文發表,
獲獎記錄
近年來，以第一/通訊作者身份在Nature Methods，Cell姊妹期刊Patterns及IEEE Transactions等刊物發表論文40餘篇，並在Springer出版人工智慧英文專著一部，申請美國發明專利4項。曾獲得IEEE Transactions on Fuzzy Systems 2020年度最佳論文獎（第一作者），世界人工智慧大會2020優秀青年論文獎，Microsoft Research Fellow，中國自動化學會優秀博士論文獎、中國人工智慧學會優秀博士論文獎，研究成果獲得5項國際學術論文獎。
2020年9月9日，榮獲2020年阿里巴巴達摩院青橙獎。
論文發表
[1] Yue Deng, Feng Bao, Steven Altschuler, Lani Wu: Scalable Analysis of Cell-type Composition from Single-cell Transcriptomics using Deep Recurrent Learning, Nature Methods, 16(4).March 2019 (影響因子:28.467)
[2] Feng Bao, Yue Deng(共同一作) et al.:Explaining the genetic causality for complex phenotypes via deep association kernel learning, Patterns, 1 AUG 2020. (Cell 姊妹期刊).
[3] Yue Deng, Zhiquan Ren, Youyong Kong, Feng Bao, Qionghai Dai. A Hierarchical Fused Fuzzy Deep Neural Network for Data Classification. IEEE Trans. on Fuzzy Systems. 25(4), 1006-1012 (2017) (IEEE Transactions on Fuzzy Systems 2020最佳論文獎)
[4] Yue Deng, Qionghai Dai, Risheng Liu, Zengke Zhang, Sanqing Hu Low-Rank Structure Learning via Nonconvex Heuristic Recovery. IEEE Trans. on Neural Netw. Learning Systems 24(3): 383-396 (2013) (IEEE智慧型計算學會亮點論文，並被科技日報報導)
[5] Yue Deng, Feng Bao, Zhiquan Ren, Youyong Kong, Qionghai Dai. Deep Direct Reinforcement Learning for Financial Signal Representation and Trading. IEEE Trans. on Neural Networks and Learning Systems. 28(3):653-664 (2017).
[6] Yue Deng, Feng Bao, Mulong Du, Meilin Wang, Qionghai Dai: Information transduction capacity reduces the uncertainties in annotation-free isoform discovery and quantification. Nucleic acids research, 45(15), e143-e143 (2017).
[7] Yue Deng, Feng Bao, Mulong Du, Zhiquan Ren, Qingzhao Zhang, Yanyu Zhao, Jinli Suo, Zhengdong Zhang, Meilin Wang, and Qionghai Dai. Probabilistic natural mapping of gene-level tests for genome-wide association studies. Briefings in bioinformatics 19(4) 545-553 (2017).
[8] Yue Deng, Youyong Kong, Feng Bao, and Qionghai Dai: Sparse coding-inspired optimal trading system for HFT industry. IEEE Trans. on Industrial Informatics 11(2), 467-475 (2015).
[9] Yue Deng, Feng Bao, Xuesong Deng, Ruiping Wang, Youyong Kong and Qionghai Dai:Deep and Structured Robust Information Theoretic Learning for Image Analysis. IEEE Transactions on Image Processing 25(9): 4209-4221 (2016)
[10] Yue Deng, Yipeng Li, Yanjun Qian, Xiangyang Ji, Qionghai Dai: Visual Words Assignment via Information-Theoretic Manifold Embedding. IEEE Trans. on Cybernetics 44(10): 1924-1937 (2014)
[11] Yue Deng, Yanyu Zhao, Zhiquan Ren, Youyong Kong, Feng Bao and Qionghai Dai. Discriminant Kernel Assignment for Image Coding. IEEETrans. on Cybernetics. 47(6), 1434-1445 (2017)
[12] Yue Deng, Yebin Liu, Qionghai Dai, Zengke Zhang, Yao Wang: Noisy Depth Maps Fusion for Multiview Stereo via Matrix Completion. IEEE Journal of Selected Topics in Signal Processing 6(5): 566-582 (2012)
[13] Yue Deng, Qionghai Dai, Zengke Zhang: Graph Laplace for Occluded Face Completion and Recognition. IEEE Trans. on Image Processing 20(8): 2329-2338 (2011)
[14] Yue Deng, Steven Altschuler, Lani Wu: PHOCOS: Inferring Multi-Feature Phenotypic Crosstalk Networks. Bioinformatics. 2016 Jun 15; 32(12):i44-i51.
[15] Yue Deng, Yilin Shen, Hongxia Jin: Adversarial Active Learning for Sequence Labeling and Generation. International Joint Conferences on Artificial Intelligence, 2018
[16] Yue Deng, Yilin Shen, Hongxia Jin: Disguise Neural Networks for Click-through Rate Prediction. International Joint Conferences on Artificial Intelligence 2017
[17] Yue Deng, Yilin Shen, Hongxia Jin: Learning Assistance from An Adversarial Critic for Multi-output Prediction, International Joint Conferences on Artificial Intelligence 2019
[18] Tian Tan, Feng Bao, Yue Deng, Alex Jin, and Qionghai Dai: Cooperative Deep Reinforcement Learning for Large-Scale Traffic Grid Signal Control. IEEE Trans. on Cybernetics (2019).
[19] Feng Bao, Yue Deng, Zhiquan Ren, Youyong Kong, Qionghai Dai: Learning Deep Landmarks for Imbalanced Data Classification. IEEE Trans. on Neural Networks and Learning Systems (2019)
