Expertises
Computer Science
- Classification Learning
- Online Portfolio
- Image Segmentation
- Models
- Receptive Field
- Vision Transformer
Economics, Econometrics and Finance
- Portfolio Selection
- Mean Reversion
Organisaties
Publicaties
2024
Are 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.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.Robust online portfolio optimization with cash flows (2024)Omega, 129. Article 103169 (E-pub ahead of print/First online). Lyu, B., Wu, B., Guo, S., Gu, J. & Ching, W.-K.https://doi.org/10.1016/j.omega.2024.103169Dynamic 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.
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.2312.04727Weighted Multivariate Mean Reversion for Online Portfolio Selection (2023)In Machine Learning and Knowledge Discovery in Databases: Research Track: European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part V (pp. 255-270) (Lecture Notes in Computer Science; Vol. 14173). Wu, B., Lyu, B. & Gu, J.https://doi.org/10.1007/978-3-031-43424-2_16Can Less Yield More? Insights into Truly Sparse Training (2023)[Contribution to conference › Poster] ICLR 2023 Workshop on Sparsity in Neural Networks. Xiao, Q., Wu, B., Yin, L., van Keulen, M. & Pechenizkiy, M.https://drive.google.com/file/d/1kbWZ9ejU9XvtOMRtAcVYmcoRCDIWj3zy/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., 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/viewMore convnets in the 2020s: Scaling up kernels beyond 51x51 using sparsity (2023)In The Eleventh International Conference on Learning Representations (ICLR 2023). OpenReview. Liu, S., Chen, T., Chen, X., Chen, X., Xiao, Q., Wu, B., Pechenizkiy, M., Mocanu, D. C. & Wang, Z.https://arxiv.org/abs/2207.03620
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.2212.09840
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