Mingjun Zhong
Mingjun Zhong
Department of Computing Science, University of Aberdeen, UK
Verified email at abdn.ac.uk
Title
Cited by
Cited by
Year
Classifying EEG for brain computer interfaces using Gaussian processes
M Zhong, F Lotte, M Girolami, A Lécuyer
Pattern Recognition Letters 29 (3), 354-359, 2008
992008
Sequence-to-point learning with neural networks for nonintrusive load monitoring
C Zhang, M Zhong, Z Wang, N Goddard, C Sutton
arXiv preprint arXiv:1612.09106, 2016
932016
Data Integration for Classification Problems Employing Gaussian Process Priors
M Girolami, M Zhong
Advances in Neural Information Processing Systems 19: Proceedings of the …, 2007
582007
Signal aggregate constraints in additive factorial HMMs, with application to energy disaggregation
M Zhong, N Goddard, C Sutton
Advances in Neural Information Processing Systems, 3590-3598, 2014
492014
A comparative evaluation of stochastic-based inference methods for Gaussian process models
M Filippone, M Zhong, M Girolami
Machine Learning 93 (1), 93-114, 2013
422013
Latent Bayesian melding for integrating individual and population models
M Zhong, N Goddard, C Sutton
Advances in neural information processing systems, 3618-3626, 2015
322015
Reversible jump MCMC for non-negative matrix factorization
M Zhong, M Girolami
Artificial Intelligence and Statistics, International Conference on (AISTATS …, 2009
282009
Bayesian methods to detect dye‐labelled DNA oligonucleotides in multiplexed Raman spectra
M Zhong, M Girolami, K Faulds, D Graham
Journal of the Royal Statistical Society: Series C (Applied Statistics) 60 …, 2011
202011
Efficient gradient-free variational inference using policy search
O Arenz, G Neumann, M Zhong
International Conference on Machine Learning, 234-243, 2018
162018
Interleaved factorial non-homogeneous hidden Markov models for energy disaggregation
M Zhong, N Goddard, C Sutton
arXiv preprint arXiv:1406.7665, 2014
162014
A variational method for learning sparse Bayesian regression
M Zhong
Neurocomputing 69 (16-18), 2351-2355, 2006
162006
An EM algorithm for learning sparse and overcomplete representations
M Zhong, H Tang, H Chen, Y Tang
Neurocomputing 57, 469-476, 2004
142004
Towards reproducible state-of-the-art energy disaggregation
N Batra, R Kukunuri, A Pandey, R Malakar, R Kumar, O Krystalakos, ...
Proceedings of the 6th ACM International Conference on Systems for Energy …, 2019
122019
Transfer learning for non-intrusive load monitoring
M D’Incecco, S Squartini, M Zhong
IEEE Transactions on Smart Grid 11 (2), 1419-1429, 2019
102019
Neural control variates for variance reduction
Z Zhu, R Wan, M Zhong
arXiv preprint arXiv:1806.00159, 2018
62018
Expectation–maximization approaches to independent component analysis
M Zhong, H Tang, Y Tang
Neurocomputing 61, 503-512, 2004
62004
A Bayesian approach to approximate joint diagonalization of square matrices
M Zhong, M Girolami
arXiv preprint arXiv:1206.4666, 2012
52012
Advances in Neural Information Processing Systems
EM Airoldi, TB Costa, SH Chan
Massachusetts Institute of Technology Press, 2009
52009
Neural Control Variates for Variance Reduction
R Wan, M Zhong, H Xiong, Z Zhu
arXiv preprint arXiv:1806.00159, 2018
32018
A hyperplane clustering algorithm for estimating the mixing matrix in sparse component analysis
X Xu, M Zhong, C Guo
Neural Processing Letters 47 (2), 475-490, 2018
22018
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