Welkom...

dr. E. Mocanu (Elena)

Universitair docent

Expertises

Engineering & Materials Science
Deep Learning
Energy Utilization
Feature Extraction
Machine Learning
Neural Networks
Reinforcement Learning
Smart Power Grids
Uncertainty

Publicaties

Recent
Liu, S., Chen, T. , Atashgahi, Z., Chen, X., Sokar, G. , Mocanu, E., Pechenizkiy, M., Wang, Z. , & Mocanu, D. C. (2022). Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity. In The Tenth International Conference on Learning Representations, ICLR 2022 OpenReview. https://openreview.net/forum?id=RLtqs6pzj1-&noteId=d7CKVDyMGZi
Liu, S., Chen, T. , Atashgahi, Z., Chen, X., Sokar, G. A. Z. N. , Mocanu, E., Pechenizkiy, M., Wang, Z. , & Mocanu, D. C. (2021). FreeTickets: Accurate, Robust and Efficient Deep Ensemble by Training with Dynamic Sparsity. Poster session presented at Sparsity in Neural Networks: Advancing Understanding and Practice 2021, Online.
Sokar, G. , Mocanu, E. , Mocanu, D. C., Pechenizkiy, M., & Stone, P. (2021). Dynamic Sparse Training for Deep Reinforcement Learning. (arXiv.org). arXiv.org.
Atashgahi, Z., Sokar, G. A. Z. N., van der Lee, T. , Mocanu, E. , Mocanu, D. C. , Veldhuis, R. N. J., & Pechenizkiy, M. (Accepted/In press). Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders (poster). Poster session presented at Sparsity in Neural Networks: Advancing Understanding and Practice 2021, Online.
Atashgahi, Z., Sokar, G. A. Z. N., van der Lee, T. , Mocanu, E. , Mocanu, D. C. , Veldhuis, R. N. J., & Pechenizkiy, M. (2021). Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders (Extended Abstract). In BNAIC/BENELEARN 2021: The 33rd Benelux Conference on Artificial Intelligence and the 30th Belgian Dutch Conference on Machine Learning
Sokar, G. A. Z. N. , Mocanu, E. , Mocanu, D. C., Pechenizkiy, M., & Stone, P. (2021). Dynamic Sparse Training for Deep Reinforcement Learning (Poster). Poster session presented at Sparsity in Neural Networks: Advancing Understanding and Practice 2021, Online.
Mocanu, D. C. , Mocanu, E., Pinto, T., Curci, S., Nguyen, P. H., Gibescu, M., Ernst, D., & Vale, Z. (2021). Sparse Training Theory for Scalable and Efficient Agents. In AAMAS '21: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (pp. 34-38) https://doi.org/10.5555/3463952.3463960
Mocanu, E. , Mocanu, D. C., Paterakis, N. G., & Gibescu, M. (2021). Forecasting. In T. Pinto, Z. Vale, & S. Widergren (Eds.), Local Electricity Markets (1 ed.). Elsevier. https://www.elsevier.com/books/local-electricity-markets/pinto/978-0-12-820074-2

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Google Scholar Link

Contactgegevens

Bezoekadres

Universiteit Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling (gebouwnr. 11)
Hallenweg 19
7522NH  Enschede

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Postadres

Universiteit Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling
Postbus 217
7500 AE Enschede