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D’Incecco, Michele, Squartini, Stefano and Zhong, Mingjun (2020) Transfer Learning for Non-Intrusive Load Monitoring. IEEE Transactions on Smart Grid, 11 (2). pp. 1419-1429. ISSN 1949-3053
Xu, Xu, Zhong, Mingjun and Guo, Chonghui (2018) A Hyperplane Clustering Algorithm for Estimating the Mixing Matrix in Sparse Component Analysis. Neural Processing Letters, 47 (475). ISSN 1370-4621
Xu, Xu, Zhong, Mingjun and Guo, Chonghui (2018) A Hyperplane Clustering Algorithm for Estimating the Mixing Matrix in Sparse Component Analysis. Neural Processing Letters, 47 (2). pp. 475-490. ISSN 1370-4621
Arenz, Oleg, Neumann, Gerhard and Zhong, Mingjun (2018) Efficient Gradient-Free Variational Inference using Policy Search. Proceedings of the 35th International Conference on Machine Learning, 80 . pp. 234-243. ISSN 1938-7228
Du, Junfu and Zhong, Mingjun (2016) Pseudo-marginal Markov Chain Monte Carlo for Nonnegative Matrix Factorization. Neural Processing Letters, 45 (553). ISSN 1370-4621
Du, Junfu and Zhong, Mingjun (2016) Pseudo-marginal Markov Chain Monte Carlo for Nonnegative Matrix Factorization. Neural Processing Letters, 45 (10). pp. 553-562. ISSN 1370-4621
Barber, Jack, Cuayahuitl, Heriberto, Zhong, Mingjun and Luan, Wempen (2020) Lightweight Non-Intrusive Load Monitoring Employing Pruned Sequence-to-Point Learning. In: 5th International Workshop on Non-Intrusive Load Monitoring, 18 November 2020, co-located with ACM BuildSys 2020.
Zhang, Chaoyun, Zhong, Mingjun, Wang, Zongzuo, Goddard, Nigel and Sutton, Charles (2018) Sequence-to-point learning with neural networks for nonintrusive load monitoring. In: The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), 2 - 7 February 2018, New Orleans.
Zhong, Mingjun, Goddard, Nigel and Sutton, Charles (2015) Latent Bayesian melding for integrating individual and population models. In: Advances in Neural Information Processing Systems, 7 - 12 December 2015, Montreal, Canada.
Zhong, Mingjun, Goddard, Nigel and Sutton, Charles (2014) Signal aggregate constraints in additive factorial HMMs, with application to energy disaggregation. In: NIPS Conference on Neural Information Processing Systems, 8 - 13 December 2014, Montreal, Canada.