dr. E. Mocanu (Elena)

Engineering & Materials Science

# Deep Learning
# Energy Utilization
# Feature Extraction
# Machine Learning
# Neural Networks
# Reinforcement Learning
# Smart Power Grids
# Uncertainty

Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), Datamanagement & Biometrics (DMB)

Xiao, Q., Wu, B., Zhang, Y., Liu, S., Pechenizkiy, M. , Mocanu, E. , & Mocanu, D. C. (2022). Dynamic Sparse Network for Time Series Classification: Learning What to “See”. In 36th Conference on Neural Information Processing Systems (NeurIPS 2022) OpenReview. https://openreview.net/forum?id=ZxOO5jfqSYw

Sokar, G. A. Z. N. , Mocanu, E. , Mocanu, D. C., Pechenizkiy, M., & Stone, P. (2022). Dynamic Sparse Training for Deep Reinforcement Learning. In L. De Raedt (Ed.), 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-&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. 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.

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Naar mijn profiel

Vakken in het huidig collegejaar worden toegevoegd op het moment dat zij definitief zijn in het Osiris systeem. Daarom kan het zijn dat de lijst nog niet compleet is voor het gehele collegejaar.