Xiaoqin Zeng, Ph.D.
Affiliations: | 2002 | Hong Kong Polytechnic University (Hong Kong) |
Area:
Computer Science, Artificial IntelligenceGoogle:
"Xiaoqin Zeng"Parents
Sign in to add mentorDaniel S. Yeung | grad student | 2002 | Hong Kong Polytechnic University (Hong Kong) | |
(Output sensitivity of MLPs derived from statistical expectation.) |
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Publications
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Xu Y, Yang J, Zeng X. (2019) An optimal time interval of input spikes involved in synaptic adjustment of spike sequence learning. Neural Networks : the Official Journal of the International Neural Network Society. 116: 11-24 |
Zeng X, Zhen Z, He J, et al. (2018) A feature selection approach based on sensitivity of RBFNNs Neurocomputing. 275: 2200-2208 |
Zhou C, Zeng X, Luo C, et al. (2016) A New Local Bipolar Autoassociative Memory Based on External Inputs of Discrete Recurrent Neural Networks With Time Delay. Ieee Transactions On Neural Networks and Learning Systems |
Huang L, Zeng X, Zhong S, et al. (2014) Sensitivity study of Binary Feedforward Neural Networks Neurocomputing. 136: 268-280 |
Yang J, Zeng X, Zhong S, et al. (2013) Effective neural network ensemble approach for improving generalization performance. Ieee Transactions On Neural Networks and Learning Systems. 24: 878-87 |
Xu Y, Zeng X, Zhong S. (2013) A new supervised learning algorithm for spiking neurons. Neural Computation. 25: 1472-511 |
Xu Y, Zeng X, Han L, et al. (2013) A supervised multi-spike learning algorithm based on gradient descent for spiking neural networks. Neural Networks : the Official Journal of the International Neural Network Society. 43: 99-113 |
Yang J, Zeng X, Zhong S. (2013) Computation of multilayer perceptron sensitivity to input perturbation Neurocomputing. 99: 390-398 |
Zhong S, Zeng X, Wu S, et al. (2012) Sensitivity-based adaptive learning rules for binary feedforward neural networks. Ieee Transactions On Neural Networks and Learning Systems. 23: 480-91 |
Zeng X, Shao J, Wang Y, et al. (2008) A sensitivity-based approach for pruning architecture of Madalines Neural Computing and Applications. 18: 957-965 |