Mingjun Zhong
Mingjun Zhong
Department of Computing Science, University of Aberdeen, UK
Verified email at abdn.ac.uk
Title
Cited by
Cited by
Year
Sequence-to-point learning with neural networks for non-intrusive load monitoring
C Zhang, M Zhong, Z Wang, N Goddard, C Sutton
Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018
2102018
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
1002008
Signal aggregate constraints in additive factorial HMMs, with application to energy disaggregation
M Zhong, N Goddard, C Sutton
Advances in Neural Information Processing Systems 27, 3590-3598, 2014
722014
Transfer learning for non-intrusive load monitoring
M D’Incecco, S Squartini, M Zhong
IEEE Transactions on Smart Grid 11 (2), 1419-1429, 2019
682019
Data Integration for Classification Problems Employing Gaussian Process Priors
M Girolami, M Zhong
Advances in Neural Information Processing Systems 19: Proceedings of the …, 2007
612007
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
512013
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
432019
Latent Bayesian melding for integrating individual and population models
M Zhong, N Goddard, C Sutton
arXiv preprint arXiv:1510.09130, 2015
382015
Reversible jump MCMC for non-negative matrix factorization
M Zhong, M Girolami
Artificial Intelligence and Statistics, International Conference on (AISTATS …, 2009
282009
Efficient gradient-free variational inference using policy search
O Arenz, G Neumann, M Zhong
International conference on machine learning, 234-243, 2018
232018
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
Interleaved factorial non-homogeneous hidden Markov models for energy disaggregation
M Zhong, N Goddard, C Sutton
arXiv preprint arXiv:1406.7665, 2014
192014
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
152004
Sequence-to-point learning with neural networks for nonintrusive load monitoring. arXiv 2016
C Zhang, M Zhong, Z Wang, N Goddard, C Sutton
arXiv preprint arXiv:1612.09106, 0
9
Trust-Region Variational Inference with Gaussian Mixture Models.
O Arenz, M Zhong, G Neumann
J. Mach. Learn. Res. 21, 163:1-163:60, 2020
82020
Neural control variates for variance reduction
Z Zhu, R Wan, M Zhong
arXiv preprint arXiv:1806.00159, 2018
72018
AREA: An adaptive reference-set based evolutionary algorithm for multiobjective optimisation
S Jiang, H Li, J Guo, M Zhong, S Yang, M Kaiser, N Krasnogor
Information Sciences 515, 365-387, 2020
62020
A demonstration of reproducible state-of-the-art energy disaggregation using NILMTK
N Batra, R Kukunuri, A Pandey, R Malakar, R Kumar, O Krystalakos, ...
Proceedings of the 6th ACM international conference on systems for energy …, 2019
62019
Neural control variates for variance reduction
R Wan, M Zhong, H Xiong, Z Zhu
arXiv preprint arXiv:1806.00159, 2018
62018
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Articles 1–20