最终学位：博士
导师类型：
电子邮箱：wendongz@sxu.edu.cn
联系电话：0351-7010566
研究方向：数据挖掘、时序预测、噪声鲁棒性
郑温冬，博士、讲师，2024年毕业于中山大学计算机学院，研究方向主要为时间序列预测、脉冲循环神经网络、轻量化神经网络的输入噪声鲁棒性。在IEEE TKDE、IEEE TNNLS、IEEE TCYB、IEEE ICCAD和ACM CIKM等计算机学会推荐的高水平期刊和会议上发表论文多篇。曾获2019年研究生国家奖学金、2020年湖南大学优秀研究生毕业生称号和2021年ACM学会CIKM会议学生资助奖。担任IEEE TPAMI、IEEE TKDE、IEEE TIP、IEEE TNNLS、IEEE TCYB、IEEE TSMCA、ACM TKDD、Neural Networks、ICML、NeurIPS、KDD、ICLR等期刊和会议的审稿人。
[1] Wendong Zheng, Putian Zhao, Gang Chen, Huihui Zhou, Yonghong Tian. A Hybrid Spiking Neurons Embedded LSTM Network for Multivariate Time Series Learning under Concept-drift Environment[J]. IEEE Transactions on Knowledge and Data Engineering, 2023.35(7): 6561-6574. (CCF-A类期刊)
[2] Wendong Zheng, Gang Chen. An Accurate GRU-based Power Time Series Prediction Approach with Selective State Updating and Stochastic Optimization[J]. IEEE Transactions on Cybernetics, 2022.52(12): 13902-13914. (CCF-B类期刊, SCI一区Top)
[3] Wendong Zheng, Jun Hu. Multivariate Time Series Prediction Based on Temporal Change Information Learning Method[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023.34(10): 7034-7048. (CCF-B类期刊, SCI一区Top)
[4] Wendong Zheng, Putian Zhao, Kai Huang, Gang Chen. Understanding the Property of Long Term Memory for the LSTM with Attention Mechanism[C]//Proc the Conference on Information and Knowledge Management (CIKM)．Queensland, Nov, 2021, pp, 2708-2717. (CCF-B类会议)
[5] Wendong Zheng, Yu Zhou, Gang Chen, Zonghua Gu, Kai Huang. Towards Effective Training of Robust Spiking Recurrent Neural Networks under General Input Noise via Provable Analysis[C]//Proc IEEE/ACM International Conference on Computer-Aided Design (ICCAD)．San Francisco, Nov, 2023, pp, 1-9. (CCF-B类会议, 电子学会I类会议)
[6] Jun Hu, Wendong Zheng. A deep learning model to effectively capture mutation information in multivariate time series prediction[J]. Knowledge-Based Systems, 2020.203: 1-20. (CCF-C类期刊, SCI一区)
[7] Jun Hu, Wendong Zheng. Multistage Attention Network for Multivariate Time Series Prediction[J]. Neurocomputing, 2020.383: 122-137. (CCF-C类期刊, SCI二区)
[8] Jun Hu, Wendong Zheng. Transformation-gated LSTM: efficient capture of short-term mutation dependencies for multivariate time series prediction tasks[C]//Proc the International Joint Conference on Neural Networks (IJCNN), Budapest, Jul, 2019, pp, 1-8. (CCF-C类会议)
南京大学计算机软件新技术国家重点实验室开放基金，面向复杂关联特性的多变量时空数据预测的深度学习方法研究，KFKT2019B09，参与