Publicaties
2025
Addressing the Collaboration Dilemma in Low-Data Federated Learning via Transient Sparsity (2025)[Working paper › Preprint]. ArXiv.org. Xiao, Q., Wu, B., Poddubnyy, A., Mocanu, E., Nguyen, P. H., Pechenizkiy, M. & Mocanu, D. C.https://doi.org/10.48550/arXiv.2506.00932NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling (2025)[Working paper › Preprint]. ArXiv.org. Grooten, B., Hasanov, F., Zhang, C., Xiao, Q., Wu, B., Atashgahi, Z., Sokar, G., Liu, S., Yin, L., Mocanu, E., Pechenizkiy, M. & Mocanu, D. C.https://doi.org/10.48550/arXiv.2505.17909Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness (2025)In 13th International Conference on Learning Representations, ICLR 2025 (pp. 28220-28246). Wu, B., Xiao, Q., Wang, S., Strisciuglio, N., Pechenizkiy, M., Keulen, M. v., Mocanu, D. C. & Mocanu, E.https://openreview.net/forum?id=daUQ7vmGap
2024
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation (2024)In NIPS '24: Proceedings of the 38th International Conference on Neural Information Processing Systems (pp. 118483-118512). Article 3762 (Advances in Neural Information Processing Systems; Vol. 37) (E-pub ahead of print/First online). Wu, B., Xiao, Q., Liu, S., Yin, L., Pechenizkiy, M., Mocanu, D. C., van Keulen, M. & Mocanu, E.https://doi.org/10.5555/3737916.3741678Robust online portfolio optimization with cash flows (2024)Omega, 129. Article 103169. Lyu, B., Wu, B., Guo, S., Gu, J. & Ching, W.-K.https://doi.org/10.1016/j.omega.2024.103169Are Sparse Neural Networks Better Hard Sample Learners? (2024)In British Machine Vision Conference (BMVC 2024). Xiao, Q., Wu, B., Yin, L., Gadzinski, C. N., Huang, T., Pechenizkiy, M. & Mocanu, D. C.Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness (2024)[Working paper › Preprint]. ArXiv.org. Wu, B., Xiao, Q., Wang, S., Strisciuglio, N., Pechenizkiy, M., van Keulen, M., Mocanu, D. C. & Mocanu, E.https://doi.org/10.48550/arXiv.2410.03030Are Sparse Neural Networks Better Hard Sample Learners? (2024)[Working paper › Preprint]. ArXiv.org. Xiao, Q., Wu, B., Yin, L., Gadzinski, C. N., Huang, T., Pechenizkiy, M. & Mocanu, D. C.https://doi.org/10.48550/arXiv.2409.09196Insights 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.Dynamic Data Pruning for Automatic Speech Recognition (2024)In Interspeech 2024 (pp. 4488-4492). Xiao, Q., Ma, P., Fernandez-Lopez, A., Wu, B., Yin, L., Petridis, S., Pechenizkiy, M., Pantic, M., Mocanu, D. C. & Liu, S.
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