Expertises
Computer Science
- Classification Learning
- Image Segmentation
- Models
- Online Portfolio
- Receptive Field
- Vision Transformer
Economics, Econometrics and Finance
- Mean Reversion
- Portfolio Selection
Organisaties
Publicaties
2023
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation. ArXiv.org. Wu, B., Xiao, Q., Liu, S., Yin, L., Pechenizkiy, M., Mocanu, D. C., Keulen, M. V. & Mocanu, E.https://doi.org/10.48550/arXiv.2312.04727Weighted Multivariate Mean Reversion for Online Portfolio SelectionIn 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). Wu, B., Lyu, B. & Gu, J.https://doi.org/10.1007/978-3-031-43424-2_16More convnets in the 2020s: Scaling up kernels beyond 51x51 using sparsityIn 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/2207.03620Dynamic Sparse Network for Time Series Classification: Learning What to “See”. 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/viewCan Less Yield More? Insights into Truly Sparse Training. Xiao, Q., Wu, B., Yin, L., van Keulen, M. & Pechenizkiy, M.https://drive.google.com/file/d/1kbWZ9ejU9XvtOMRtAcVYmcoRCDIWj3zy/view
2022
Dynamic Sparse Network for Time Series Classification: Learning What to "see''. 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”. Xiao, Q., Wu, B., Zhang, Y., Liu, S., Pechenizkiy, M., Mocanu, E. & Mocanu, D. C.https://openreview.net/forum?id=ZxOO5jfqSYwMore ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity. ArXiv.org. Liu, S., Chen, T., Chen, X., Chen, X., Xiao, Q., Wu, B., Kärkkäinen, T., Pechenizkiy, M., Mocanu, D. & Wang, Z.https://doi.org/10.48550/arXiv.2207.03620
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