| 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 | 651 | 2018 |
| Transfer learning for non-intrusive load monitoring M D’Incecco, S Squartini, M Zhong IEEE Transactions on Smart Grid 11 (2), 1419-1429, 2019 | 307 | 2019 |
| 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 | 138 | 2019 |
| 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 | 116 | 2008 |
| 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, 2014 | 111 | 2014 |
| A comparative evaluation of stochastic-based inference methods for Gaussian process models M Filippone, M Zhong, M Girolami Machine Learning 93, 93-114, 2013 | 71 | 2013 |
| Data Integration for Classification Problems Employing Gaussian Process Priors M Girolami, M Zhong Advances in Neural Information Processing Systems 19: Proceedings of the …, 2007 | 64 | 2007 |
| The IDEAL household energy dataset, electricity, gas, contextual sensor data and survey data for 255 UK homes M Pullinger, J Kilgour, N Goddard, N Berliner, L Webb, M Dzikovska, ... Scientific Data 8 (1), 146, 2021 | 52 | 2021 |
| Latent Bayesian melding for integrating individual and population models M Zhong, N Goddard, C Sutton Advances in neural information processing systems 28, 2015 | 52 | 2015 |
| Efficient gradient-free variational inference using policy search O Arenz, G Neumann, M Zhong International conference on machine learning, 234-243, 2018 | 42 | 2018 |
| Lightweight non-intrusive load monitoring employing pruned sequence-to-point learning J Barber, H Cuayáhuitl, M Zhong, W Luan Proceedings of the 5th international workshop on non-intrusive load …, 2020 | 40 | 2020 |
| Classification of normal/abnormal heart sound recordings based on multi-domain features and back propagation neural network H Tang, H Chen, T Li, M Zhong 2016 Computing in Cardiology Conference (CinC), 593-596, 2016 | 40 | 2016 |
| Reversible jump MCMC for non-negative matrix factorization M Zhong, M Girolami Artificial Intelligence and Statistics, International Conference on (AISTATS …, 2009 | 29 | 2009 |
| Interleaved factorial non-homogeneous hidden Markov models for energy disaggregation M Zhong, N Goddard, C Sutton arXiv preprint arXiv:1406.7665, 2014 | 24 | 2014 |
| 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 (2 …, 2011 | 24 | 2011 |
| Nonintrusive load monitoring based on self-supervised learning S Chen, B Zhao, M Zhong, W Luan, Y Yu IEEE Transactions on Instrumentation and Measurement 72, 1-13, 2023 | 23 | 2023 |
| Lightweight deep learning methods for panoramic dental X-ray image segmentation S Lin, X Hao, Y Liu, D Yan, J Liu, M Zhong Neural Computing and Applications 35 (11), 8295-8306, 2023 | 22 | 2023 |
| 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 | 22 | 2020 |
| Interpretation and reporting of predictive or diagnostic machine-learning research in Trauma & Orthopaedics L Farrow, M Zhong, GP Ashcroft, L Anderson, RMD Meek The Bone & Joint Journal 103 (12), 1754-1758, 2021 | 21 | 2021 |
| Neural control variates for Monte Carlo variance reduction R Wan, M Zhong, H Xiong, Z Zhu Joint European Conference on Machine Learning and Knowledge Discovery in …, 2019 | 21 | 2019 |
Charles SuttonGoogle, University of EdinburghPreverjeni e-poštni naslov na inf.ed.ac.uk
