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2020 – today
- 2021
[j7]Changxin Huang
, Ronghui Zhang, Meizi Ouyang, Pengxu Wei, Junfan Lin, Jiang Su, Liang Lin:
Deductive Reinforcement Learning for Visual Autonomous Urban Driving Navigation. IEEE Trans. Neural Networks Learn. Syst. 32(12): 5379-5391 (2021)- [i4]Changxin Huang, Jiang Su, Zhihong Zhang, Dong Zhao, Liang Lin:
Hybrid and dynamic policy gradient optimization for bipedal robot locomotion. CoRR abs/2107.01908 (2021) - 2020
- [c23]Bailin Li, Bowen Wu, Jiang Su, Guangrun Wang:
EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning. ECCV (2) 2020: 639-654 - [i3]Bailin Li, Bowen Wu, Jiang Su, Guangrun Wang, Liang Lin:
EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning. CoRR abs/2007.02491 (2020)
2010 – 2019
- 2019
- [j6]Huamei Qi, Jiang Tao, Jiang Su, Zhiwen Zeng, Wangping Xiong:
QoS adaptive and energy aware cross-layer opportunistic routing protocol in wireless sensor networks. IET Commun. 13(8): 1034-1042 (2019) - 2018
- [b1]Jiang Su:
Artificial neural networks acceleration on field-programmable gate arrays considering model redundancy. Imperial College London, UK, 2018 - [j5]Huamei Qi, Fengqi Liu, Tailong Xiao, Jiang Su:
A Robust and Energy-Efficient Weighted Clustering Algorithm on Mobile Ad Hoc Sensor Networks. Algorithms 11(8): 116 (2018) - [c22]Jiang Su, Julian Faraone, Junyi Liu, Yiren Zhao, David B. Thomas, Philip Heng Wai Leong, Peter Y. K. Cheung:
Redundancy-Reduced MobileNet Acceleration on Reconfigurable Logic for ImageNet Classification. ARC 2018: 16-28 - [c21]Jiang Su, Nicholas J. Fraser, Giulio Gambardella, Michaela Blott, Gianluca Durelli, David B. Thomas, Philip Heng Wai Leong, Peter Y. K. Cheung:
Accuracy to Throughput Trade-Offs for Reduced Precision Neural Networks on Reconfigurable Logic. ARC 2018: 29-42 - [i2]Jiang Su, Nicholas J. Fraser, Giulio Gambardella, Michaela Blott, Gianluca Durelli, David B. Thomas, Philip Heng Wai Leong, Peter Y. K. Cheung:
Accuracy to Throughput Trade-offs for Reduced Precision Neural Networks on Reconfigurable Logic. CoRR abs/1807.10577 (2018) - 2017
- [c20]Huamei Qi, Tailong Xiao, Anfeng Liu, Jiang Su:
Toward Energy-Efficient and Robust Clustering Algorithm on Mobile Ad Hoc Sensor Networks. COCOA (2) 2017: 182-195 - [i1]Hongyu Guo, Colin Cherry, Jiang Su:
End-to-End Multi-View Networks for Text Classification. CoRR abs/1704.05907 (2017) - 2016
- [j4]Jiang Su, Jianxiong Liu, David B. Thomas, Peter Y. K. Cheung:
Neural Network Based Reinforcement Learning Acceleration on FPGA Platforms. SIGARCH Comput. Archit. News 44(4): 68-73 (2016) - [c19]Jiang Su, David B. Thomas, Peter Y. K. Cheung:
Increasing Network Size and Training Throughput of FPGA Restricted Boltzmann Machines Using Dropout. FCCM 2016: 48-51 - 2012
- [j3]Jin Huang, Jelber Sayyad-Shirabad, Stan Matwin, Jiang Su:
Improving multi-view semi-supervised learning with agreement-based sampling. Intell. Data Anal. 16(5): 745-761 (2012) - [c18]Jiang Su, Le Dong, Peng Ren, Edwin R. Hancock:
Hypergraph matching based on Marginalized Constrained Compatibility. ICPR 2012: 2922-2925 - 2011
- [c17]Jiang Su, Jelber Sayyad Shirab, Stan Matwin:
Large Scale Text Classification using Semisupervised Multinomial Naive Bayes. ICML 2011: 97-104 - 2010
- [c16]Jin Huang, Jelber Sayyad-Shirabad, Stan Matwin, Jiang Su:
Improving Co-training with Agreement-Based Sampling. RSCTC 2010: 197-206
2000 – 2009
- 2009
- [c15]Jiang Su, Jelber Sayyad-Shirabad, Stan Matwin, Jin Huang:
Active Learning with Automatic Soft Labeling for Induction of Decision Trees. Canadian AI 2009: 241-244 - 2008
- [j2]Harry Zhang, Jiang Su:
Naive Bayes for optimal ranking. J. Exp. Theor. Artif. Intell. 20(2): 79-93 (2008) - [c14]Jiang Su, Harry Zhang, Charles X. Ling, Stan Matwin:
Discriminative parameter learning for Bayesian networks. ICML 2008: 1016-1023 - 2006
- [j1]Harry Zhang, Jiang Su:
Learning probabilistic decision trees for AUC. Pattern Recognit. Lett. 27(8): 892-899 (2006) - [c13]Jiang Su, Harry Zhang:
A Fast Decision Tree Learning Algorithm. AAAI 2006: 500-505 - [c12]Jiang Su, Harry Zhang:
Probabilistic Inference Trees for Classification and Ranking. Canadian AI 2006: 526-537 - [c11]Jiang Su, Harry Zhang:
Full Bayesian network classifiers. ICML 2006: 897-904 - 2005
- [c10]Jiang Su, Harry Zhang:
Representing Conditional Independence Using Decision Trees. AAAI 2005: 874-879 - [c9]Harry Zhang, Liangxiao Jiang, Jiang Su:
Hidden Naive Bayes. AAAI 2005: 919-924 - [c8]Liangxiao Jiang, Harry Zhang, Jiang Su:
Learning k-Nearest Neighbor Naive Bayes for Ranking. ADMA 2005: 175-185 - [c7]Liangxiao Jiang, Harry Zhang, Zhihua Cai, Jiang Su:
One Dependence Augmented Naive Bayes. ADMA 2005: 186-194 - [c6]Liangxiao Jiang, Harry Zhang, Jiang Su:
Instance Cloning Local Naive Bayes. Canadian AI 2005: 280-291 - [c5]Liangxiao Jiang, Harry Zhang, Zhihua Cai, Jiang Su:
Learning Tree Augmented Naive Bayes for Ranking. DASFAA 2005: 688-698 - [c4]Harry Zhang, Liangxiao Jiang, Jiang Su:
Augmenting naive Bayes for ranking. ICML 2005: 1020-1027 - 2004
- [c3]Harry Zhang, Jiang Su:
Naive Bayesian Classifiers for Ranking. ECML 2004: 501-512 - [c2]Harry Zhang, Jiang Su:
Conditional Independence Trees. ECML 2004: 513-524 - [c1]Jiang Su, Harry Zhang:
Learning Conditional Independence Tree for Ranking. ICDM 2004: 531-534
Coauthor Index
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