| Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science DC Mocanu, E Mocanu, P Stone, PH Nguyen, M Gibescu, A Liotta Nature communications 9 (1), 1-12, 2017 | 658 | 2017 |
| Deep learning for estimating building energy consumption E Mocanu, PH Nguyen, M Gibescu, WL Kling Sustainable Energy, Grids and Networks 6, 91-99, 2016 | 650 | 2016 |
| On-line building energy optimization using deep reinforcement learning E Mocanu, DC Mocanu, PH Nguyen, A Liotta, ME Webber, M Gibescu, ... IEEE transactions on smart grid 10 (4), 3698-3708, 2018 | 599 | 2018 |
| Unsupervised energy prediction in a Smart Grid context using reinforcement cross-building transfer learning E Mocanu, PH Nguyen, WL Kling, M Gibescu Energy and Buildings 116, 646-655, 2016 | 169 | 2016 |
| Deep learning versus traditional machine learning methods for aggregated energy demand prediction NG Paterakis, E Mocanu, M Gibescu, B Stappers, W van Alst 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT …, 2017 | 122 | 2017 |
| A topological insight into restricted Boltzmann machines DC Mocanu, E Mocanu, PH Nguyen, M Gibescu, A Liotta Machine Learning 104 (2), 243-270, 2016 | 112 | 2016 |
| Enabling cooperative behavior for building demand response based on extended joint action learning LA Hurtado, E Mocanu, PH Nguyen, M Gibescu, RIG Kamphuis IEEE Transactions on Industrial Informatics 14 (1), 127-136, 2017 | 75 | 2017 |
| Big data application in power systems R Arghandeh, Y Zhou Elsevier, 2017 | 69 | 2017 |
| Comparison of machine learning methods for estimating energy consumption in buildings E Mocanu, PH Nguyen, M Gibescu, WL Kling 2014 international conference on probabilistic methods applied to power …, 2014 | 65 | 2014 |
| Big IoT data mining for real-time energy disaggregation in buildings DC Mocanu, E Mocanu, PH Nguyen, M Gibescu, A Liotta 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2016 | 60 | 2016 |
| Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity S Liu, T Chen, Z Atashgahi, X Chen, G Sokar, E Mocanu, M Pechenizkiy, ... International Conference on Learning Representations ICLR 2022, arXiv …, 2022 | 52 | 2022 |
| Demand forecasting at low aggregation levels using factored conditional restricted Boltzmann machine E Mocanu, PH Nguyen, M Gibescu, EM Larsen, P Pinson 2016 Power Systems Computation Conference (PSCC), 1-7, 2016 | 37 | 2016 |
| Dynamic Sparse Training for Deep Reinforcement Learning G Sokar, E Mocanu, DC Mocanu, M Pechenizkiy, P Stone IJCAI-ECAI 2022, 31st International Joint Conference on Artificial …, 2022 | 35 | 2022 |
| Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders Z Atashgahi, G Sokar, T van der Lee, E Mocanu, DC Mocanu, R Veldhuis, ... Machine Learning Journal (ECML-PKDD 2022 journal track), 2022 | 34 | 2022 |
| Energy disaggregation for real-time building flexibility detection E Mocanu, PH Nguyen, M Gibescu 2016 IEEE Power and Energy Society General Meeting (PESGM), 1-5, 2016 | 34 | 2016 |
| Deep learning for power system data analysis E Mocanu, HP Nguyen, M Gibescu Book chapter in Big data application in power systems, 2017 | 31 | 2017 |
| Forecasting E Mocanu, DC Mocanu, NG Paterakis, M Gibescu Local Electricity Markets, 243-257, 2021 | 30* | 2021 |
| Sparse Training Theory for Scalable and Efficient Agents DC Mocanu, E Mocanu, T Pinto, S Curci, PH Nguyen, M Gibescu, D Ernst, ... 20th International Conference on Autonomous Agents and Multiagent Systems …, 2021 | 25 | 2021 |
| Comfort-constrained demand flexibility management for building aggregations using a decentralized approach LA Hurtado, E Mocanu, PH Nguyen, M Gibescu, WL Kling 2015 International Conference on Smart Cities and Green ICT Systems …, 2015 | 24 | 2015 |
| Dynamic Sparse Network for Time Series Classification: Learning What to" see'' Q Xiao, B Wu, Y Zhang, S Liu, M Pechenizkiy, E Mocanu, DC Mocanu NeurIPS 2022, arXiv:2212.09840, 2022 | 21 | 2022 |
Madeleine GibescuProfessor of Intermittent Renewable Energy, Utrecht University在 uu.nl 的电子邮件经过验证
