Expertise
Engineering & Materials Science
# Deep Learning
# Energy Utilization
# Feature Extraction
# Machine Learning
# Neural Networks
# Reinforcement Learning
# Smart Power Grids
# Uncertainty
Organisations
Publications
Recent
Sokar, G. A. Z. N.
, Mocanu, E.
, Mocanu, D. C., Pechenizkiy, M., & Stone, P. (2022).
Dynamic Sparse Training for Deep Reinforcement Learning. In
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022 (pp. 3437-3443)
https://doi.org/10.24963/ijcai.2022/477
Grooten, B. J., Sokar, G.
, Mocanu, E., Dohare, S., Taylor, M. E., Pechenizkiy, M.
, & Mocanu, D. C. (2022).
Towards Implementing Truly Sparse Connections in Deep RL Agents. Poster session presented at Sparsity in Neural Networks: Advancing Understanding and Practice 2022, Virtual + ICML meetup.
Atashgahi, Z., Sokar, G., van der Lee, T.
, Mocanu, E.
, Mocanu, D. C.
, Veldhuis, R., & Pechenizkiy, M. (2022).
Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders.
Machine Learning,
111, 377–414.
https://doi.org/10.1007/s10994-021-06063-x
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-¬eId=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. 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.
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
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.
Sokar, G.
, Mocanu, E.
, Mocanu, D. C., Pechenizkiy, M., & Stone, P. (2021).
Dynamic Sparse Training for Deep Reinforcement Learning. (arXiv.org). arXiv.org.
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
UT Research Information System
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Affiliated Study Programmes
Master
Courses Academic Year 2022/2023
Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.
Courses Academic Year 2021/2022
Contact Details
Visiting Address
University of Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling
(building no. 11)
Hallenweg 19
7522NH Enschede
The Netherlands
Mailing Address
University of Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling
P.O. Box 217
7500 AE Enschede
The Netherlands