B. Wu MSc (Boqian)

Wu, B., Xiao, Q., Liu, S., Yin, L., Pechenizkiy, M., Mocanu, D. C. , Keulen, M. V. , & Mocanu, E. (2023). E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation. ArXiv.org. https://doi.org/10.48550/arXiv.2312.04727

Wu, B., Lyu, B., & Gu, J. (2023). Weighted Multivariate Mean Reversion for Online Portfolio Selection. In D. Koutra, C. Plant, M. Gomez Rodriguez, E. Baralis, & F. Bonchi (Eds.), 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). https://doi.org/10.1007/978-3-031-43424-2_16

Xiao, Q. , Wu, B., Yin, L. , van Keulen, M., & Pechenizkiy, M. (2023). Can Less Yield More? Insights into Truly Sparse Training. Poster session presented at ICLR 2023 Workshop on Sparsity in Neural Networks, Kigali, Rwanda. https://drive.google.com/file/d/1kbWZ9ejU9XvtOMRtAcVYmcoRCDIWj3zy/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

Liu, S., Chen, T., Chen, X., Chen, X., Xiao, Q. , Wu, B., Pechenizkiy, M. , Mocanu, D. C., & Wang, Z. (2023). More convnets in the 2020s: Scaling up kernels beyond 51x51 using sparsity. In The Eleventh International Conference on Learning Representations (ICLR 2023) OpenReview. https://arxiv.org/abs/2207.03620

Liu, S., Chen, T., Chen, X., Chen, X., Xiao, Q. , Wu, B., Kärkkäinen, T., Pechenizkiy, M. , Mocanu, D., & Wang, Z. (2022). More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity. ArXiv.org. https://doi.org/10.48550/arXiv.2207.03620

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''. ArXiv.org. https://doi.org/10.48550/arXiv.2212.09840

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