• Source: Scopus
20142022

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  • 2021

    A Lightweight Neural Network for Energy Disaggregation Employing Depthwise Separable Convolution

    Zhang, R., Luan, W., Liu, B. & Zhong, M., 2021, Proceedings - 2021 IEEE Sustainable Power and Energy Conference: Energy Transition for Carbon Neutrality, iSPEC 2021. Institute of Electrical and Electronics Engineers Inc., p. 4109-4114 6 p. (Proceedings - 2021 IEEE Sustainable Power and Energy Conference: Energy Transition for Carbon Neutrality, iSPEC 2021).

    Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

  • Load Disaggregation Based on Sequence-to-point Network with Unsupervised Pre-training

    Chen, S., Zhao, B., Luan, W. & Zhong, M., 2021, 5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021. Institute of Electrical and Electronics Engineers Inc., p. 3224-3229 6 p. (5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021).

    Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

  • 2020

    Lightweight Non-Intrusive Load Monitoring Employing Pruned Sequence-to-Point Learning

    Barber, J., Cuayáhuitl, H., Zhong, M. & Luan, W., 18 Nov 2020, NILM 2020 - Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring. Association for Computing Machinery, Inc, p. 11-15 5 p. (NILM 2020 - Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring).

    Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

    8 Citations (Scopus)
  • Neural Control Variates for Monte Carlo Variance Reduction

    Wan, R., Zhong, M., Xiong, H. & Zhu, Z., 1 Jan 2020, Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2019, Proceedings. Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M. & Robardet, C. (eds.). Springer , p. 533-547 15 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11907 LNAI).

    Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

    2 Citations (Scopus)
  • 2019

    A demonstration of reproducible state-of-the-art energy disaggregation using NILMTK

    Batra, N., Kukunuri, R., Pandey, A., Malakar, R., Kumar, R., Krystalakos, O., Zhong, M., Meira, P. & Parson, O., 13 Nov 2019, BuildSys 2019 - Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. Association for Computing Machinery, Inc, p. 358-359 2 p. (BuildSys 2019 - Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation).

    Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

    8 Citations (Scopus)
  • 2018

    Efficient Gradient-Free Variational Inference using Policy Search

    Arenz, O., Neumann, G. & Zhong, M., 1 Jul 2018, Proceedings of the 35th International Conference on Machine Learning. Dy, J. & Krause, A. (eds.). MLR Press, Vol. 80. p. 234-243 10 p. (Proceedings of Machine Learning Research).

    Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

    Open Access
  • Sequence-to-Point Learning with Neural Networks for Non-Intrusive Load Monitoring

    Zhang, C., Zhong, M., Wang, Z., Goddard, N. & Sutton, C., 26 Apr 2018, Thirty-second AAAI conference on artificial intelligence. Palo Alto, California USA: AIII Press, p. 2604-2611 8 p.

    Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

    Open Access
  • Computational Learning for Autonomous Systems, TU Darmstadt, Darmstadt, Germany

    Arenz, O., Zhong, M. & Neumann, G., 1 Jan 2018, 35th International Conference on Machine Learning, ICML 2018. Krause, A. & Dy, J. (eds.). International Machine Learning Society (IMLS), p. 359-371 13 p. (35th International Conference on Machine Learning, ICML 2018; vol. 1).

    Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

  • Sequence-to-point learning with neural networks for non-intrusive load monitoring

    Zhang, C., Zhong, M., Wang, Z., Goddard, N. & Sutton, C., 1 Jan 2018, 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. AAAI Press, p. 2604-2611 8 p. (32nd AAAI Conference on Artificial Intelligence, AAAI 2018).

    Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

    169 Citations (Scopus)
  • 2014

    Signal aggregate constraints in additive factorial HMMs, with application to energy disaggregation

    Zhong, M., Goddard, N. & Sutton, C., Dec 2014, Advances in Neural Information Processing Systems 27 (NIPS 2014). Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D. & Weinberger, K. Q. (eds.). Palais des Congrès de Montréal, Montréal, CANADA: Curran Associates, Inc., Vol. 4. p. 3590-3598 9 p. (Advances in Neural Information Processing Systems).

    Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

    Open Access
  • Signal aggregate constraints in additive factorial HMMs, with application to energy disaggregation

    Zhong, M., Goddard, N. & Sutton, C., 1 Jan 2014, Advances in Neural Information Processing Systems 27 (NIPS 2014). Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D. & Weinberger, K. Q. (eds.). Palais des Congrès de Montréal, Montréal, CANADA: Curran Associates, Inc., Vol. 4. p. 3590-3598 9 p. (Advances in Neural Information Processing Systems).

    Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

    Open Access
    49 Citations (Scopus)