Mingjun Zhong
Mingjun Zhong
School of Computer Science, University of Lincoln, UK
Email verificata su Lincoln.ac.uk
TitoloCitata daAnno
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
942008
Data Integration for Classification Problems Employing Gaussian Process Priors
M Girolami, M Zhong
Advances in Neural Information Processing Systems 19: Proceedings of the …, 2007
522007
Sequence-to-point learning with neural networks for non-intrusive load monitoring
C Zhang, M Zhong, Z Wang, N Goddard, C Sutton
Thirty-Second AAAI Conference on Artificial Intelligence, 2018
452018
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
392014
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
382013
Latent Bayesian melding for integrating individual and population models
M Zhong, N Goddard, C Sutton
Advances in neural information processing systems, 3618-3626, 2015
272015
Reversible jump MCMC for non-negative matrix factorization
M Zhong, M Girolami
Artificial Intelligence and Statistics, International Conference on (AISTATS …, 2009
262009
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
182011
A variational method for learning sparse Bayesian regression
M Zhong
Neurocomputing 69 (16-18), 2351-2355, 2006
142006
Interleaved factorial non-homogeneous hidden Markov models for energy disaggregation
M Zhong, N Goddard, C Sutton
arXiv preprint arXiv:1406.7665, 2014
132014
An EM algorithm for learning sparse and overcomplete representations
M Zhong, H Tang, H Chen, Y Tang
Neurocomputing 57, 469-476, 2004
132004
Neural control variates for variance reduction
Z Zhu, R Wan, M Zhong
arXiv preprint arXiv:1806.00159, 2018
82018
Efficient gradient-free variational inference using policy search
O Arenz, M Zhong, G Neumann
82018
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
Blind source separation for fmri signals using spatial independent component analysis
M Zhong, H Tang, Y Tang
Shengwu Wuli Xuebao 19 (1), 79-83, 2003
32003
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
Bayesian Analysis for miRNA and mRNA Interactions Using Expression Data
M Zhong, R Liu, B Liu
arXiv preprint arXiv:1210.3456, 2012
22012
On the fully Bayesian treatment of latent Gaussian models using stochastic simulations
M Filippone, M Zhong, M Girolami
Technical Report TR-2012-329, School of Computing Science, University of Glasgow, 2012
22012
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
Articoli 1–20