| 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 | 133 | 2018 |
| 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 | 99 | 2008 |
| Data Integration for Classification Problems Employing Gaussian Process Priors M Girolami, M Zhong Advances in Neural Information Processing Systems 19: Proceedings of the …, 2007 | 59 | 2007 |
| 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 | 55 | 2014 |
| 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 | 46 | 2013 |
| Latent Bayesian melding for integrating individual and population models M Zhong, N Goddard, C Sutton Advances in neural information processing systems 28, 3618-3626, 2015 | 34 | 2015 |
| Transfer learning for non-intrusive load monitoring M D’Incecco, S Squartini, M Zhong IEEE Transactions on Smart Grid 11 (2), 1419-1429, 2019 | 28 | 2019 |
| Reversible jump MCMC for non-negative matrix factorization M Zhong, M Girolami Artificial Intelligence and Statistics, International Conference on (AISTATS …, 2009 | 28 | 2009 |
| 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 | 26 | 2019 |
| 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 | 20 | 2011 |
| Interleaved factorial non-homogeneous hidden Markov models for energy disaggregation M Zhong, N Goddard, C Sutton arXiv preprint arXiv:1406.7665, 2014 | 18 | 2014 |
| Efficient gradient-free variational inference using policy search O Arenz, G Neumann, M Zhong International conference on machine learning, 234-243, 2018 | 16 | 2018 |
| A variational method for learning sparse Bayesian regression M Zhong Neurocomputing 69 (16-18), 2351-2355, 2006 | 16 | 2006 |
| An EM algorithm for learning sparse and overcomplete representations M Zhong, H Tang, H Chen, Y Tang Neurocomputing 57, 469-476, 2004 | 14 | 2004 |
| Neural control variates for variance reduction Z Zhu, R Wan, M Zhong arXiv preprint arXiv:1806.00159, 2018 | 6 | 2018 |
| Expectation–maximization approaches to independent component analysis M Zhong, H Tang, Y Tang Neurocomputing 61, 503-512, 2004 | 6 | 2004 |
| A Bayesian approach to approximate joint diagonalization of square matrices M Zhong, M Girolami arXiv preprint arXiv:1206.4666, 2012 | 5 | 2012 |
| Advances in Neural Information Processing Systems EM Airoldi, TB Costa, SH Chan Massachusetts Institute of Technology Press, 2009 | 5 | 2009 |
| Neural Control Variates for Variance Reduction R Wan, M Zhong, H Xiong, Z Zhu arXiv preprint arXiv:1806.00159, 2018 | 4 | 2018 |
| 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 | 2 | 2019 |
Mark GirolamiUniversity of CambridgeVerified email at cam.ac.uk
