金融科技、金融工程与风险管理

刘贵松

教授

刘贵松，教授。研究方向包括类脑计算与机器学习、大数据分析技术、模式识别和金融科技。

类脑计算与机器学习大数据分析技术模式识别金融科技

教育背景

Human-Level Control Through Directly Trained Deep Spiking Q-Networks. IEEE Transactions on Cybernetics, 2023, online.
Bio-inspired Active Learning method in spiking neural network. Knowledge-Based Systems, 2023, 261: 110193.
一种基于损失预测的双主动域适应算法研究. 计算机学报，2023：579–593。
Effective transfer learning algorithm in spiking neural networks. IEEE Transactions on Cybernetics, 2022, 52(12): 13323–13335.
Consortium Blockchain-Based Spectrum Trading for Network Slicing in 5G RAN: A Multi-Agent Deep Reinforcement Learning Approach. IEEE Transactions on Mobile Computing, 2022, online.
Multi-source sequential knowledge regression by using transfer RNN units. Neural Networks, 2019, 119: 151–161.