Publicaties

2024

Insights into Dynamic Sparse Training: Theory Meets Practice (2024)[Contribution to conference › Poster] European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2024. Wu, B., van Keulen, M., Mocanu, D. C. & Mocanu, E.

2023

E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation (2023)[Working paper › Preprint]. ArXiv.org. Wu, B., Xiao, Q., Liu, S., Yin, L., Pechenizkiy, M., Mocanu, D. C., van Keulen, M. & Mocanu, E.https://doi.org/10.48550/arXiv.2312.04727Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning (2023)In AAMAS '23: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems (pp. 1932-1941) (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS; Vol. 2023). ACM Press. Grooten, B., Sokar, G., Dohare, S., Mocanu, E., Taylor, M. E., Pechenizkiy, M. & Mocanu, D. C.https://dl.acm.org./doi/10.5555/3545946.3598862Dynamic Sparse Network for Time Series Classification: Learning What to “See” (2023)[Contribution to conference › Poster] ICLR 2023 Workshop on Sparsity in Neural Networks. Xiao, Q., Wu, B., Zhang, Y., Liu, S., Pechenizkiy, M., Mocanu, E. & Mocanu, D. C.https://drive.google.com/file/d/10pxPf2aWTdMumUba_8-7v_jEZ3-K_uV3/viewDynamic Sparse Network for Time Series Classification: Learning What to “See” (2023)[Contribution to conference › Poster] ICLR 2023 Workshop on Sparsity in Neural Networks. Xiao, Q., Zhang, Y., Liu, S., Pechenizkiy, M., Mocanu, E. & Mocanu, D. C.https://drive.google.com/file/d/10pxPf2aWTdMumUba_8-7v_jEZ3-K_uV3/viewAutomatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning (2023)[Working paper › Preprint]. ArXiv.org. Grooten, B., Sokar, G., Dohare, S., Mocanu, E., Taylor, M. E., Pechenizkiy, M. & Mocanu, D. C.https://doi.org/10.48550/arXiv.2302.06548Enhancing Learning in Sparse Neural Networks: A Hebbian Learning Approach (2023)In BNAIC/BENELEARN 2023. de Ranitz, A., Beldad, A. D. & Mocanu, E.

2022

Dynamic Sparse Network for Time Series Classification: Learning What to "see'' (2022)[Working paper › Preprint]. ArXiv.org. Xiao, Q., Wu, B., Zhang, Y., Liu, S., Pechenizkiy, M., Mocanu, E. & Mocanu, D. C.https://doi.org/10.48550/arXiv.2212.09840Dynamic Sparse Network for Time Series Classification: Learning What to “See” (2022)[Contribution to conference › Paper] 36th Annual Conference on Neural Information Processing Systems, NeurIPS 2022. Xiao, Q., Wu, B., Zhang, Y., Liu, S., Pechenizkiy, M., Mocanu, E. & Mocanu, D. C.https://openreview.net/forum?id=ZxOO5jfqSYwTowards Implementing Truly Sparse Connections in Deep RL Agents (2022)[Contribution to conference › Poster] Sparsity in Neural Networks: Advancing Understanding and Practice 2022. Grooten, B. J., Sokar, G., Mocanu, E., Dohare, S., Taylor, M. E., Pechenizkiy, M. & Mocanu, D. C.

Onderzoeksprofielen

Vakken collegejaar 2024/2025

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.

Vakken collegejaar 2023/2024

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