dr. E. Mocanu (Elena)

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

de Ranitz, A. , Beldad, A. D. , & Mocanu, E. (2023). Enhancing Learning in Sparse Neural Networks: A Hebbian Learning Approach. In BNAIC/BENELEARN 2023

Grooten, B., Sokar, G., Dohare, S. , Mocanu, E., Taylor, M. E., Pechenizkiy, M. , & Mocanu, D. C. (2023). Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning. In A. Ricci, W. Yeoh, N. Agmon, & B. An (Eds.), AAMAS '23: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems (Vol. 2023-May, pp. 1932-1941). (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS). ACM Press.

Xiao, Q., Zhang, Y., Liu, S., Pechenizkiy, M. , Mocanu, E. , & Mocanu, D. C. (2023). Dynamic Sparse Network for Time Series Classification: Learning What to “See”. Paper presented at ICLR 2023 Workshop on Sparsity in Neural Networks, Kigali, Rwanda. https://drive.google.com/file/d/10pxPf2aWTdMumUba_8-7v_jEZ3-K_uV3/view

Xiao, Q. , Wu, B., Zhang, Y., Liu, S., Pechenizkiy, M. , Mocanu, E. , & Mocanu, D. C. (2023). Dynamic Sparse Network for Time Series Classification: Learning What to “See”. Poster session presented at ICLR 2023 Workshop on Sparsity in Neural Networks, Kigali, Rwanda. https://drive.google.com/file/d/10pxPf2aWTdMumUba_8-7v_jEZ3-K_uV3/view

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”. Paper presented at 36th Annual Conference on Neural Information Processing Systems, NeurIPS 2022, New Orleans, Louisiana, United States. 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. Advance online publication. 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.

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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.