Bojian Yin

Full Name

B. Yin

Email

Bojian.Yin@cwi.nl

Room

M129

Department(s)

Machine Learning

Function(s)

Researcher

Bojian Yin

Sun, T., Yin, B., & Bohte, S. (2023). Efficient uncertainty estimation in Spiking Neural Networks via MC-dropout. In Proceedings of the International Conference of Artificial Neural Networks (pp. 393–406). doi:10.1007/978-3-031-44207-0_33
Yin, B., Corradi, F., & Bohte, S. (2023). Accurate online training of dynamical spiking neural networks through Forward Propagation Through Time. Nature Machine Intelligence. doi:10.1038/s42256-023-00650-4
Yin, B. (2022, December 14). Efficient and accurate spiking neural networks.
Vicol, A.-D., Yin, B., & Bohte, S. (2022). Real-time classification of LIDAR data using discrete-time Recurrent Spiking Neural Networks. In Proceedings of the IEEE International Joint Conference on Neural Networks (pp. 1–9). doi:10.1109/IJCNN55064.2022.9892006
Yin, B., Guo, Q., Corporaal, H., Corradi, F., & Bohte, S. (2022). Attentive decision-making and dynamic resetting of continual running SRNNs for end-to-end streaming keyword spotting. In Proceedings of the International Conference on Neuromorphic Systems (pp. 1–8). doi:10.1145/3546790.3546795
Yin, B., Corradi, F., & Bohte, S. (2021). Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks. Nature Machine Intelligence, 3(10), 905–913. doi:10.1038/s42256-021-00397-w
Yin, B., Scholte, S., & Bohte, S. (2021). LocalNorm: Robust image classification through dynamically regularized normalization. In Proceedings of the International Conference of Artificial Neural Networks (pp. 240–252). doi:10.1007/978-3-030-86380-7_20
Yin, B., Corradi, F., & Bohte, S. (2020). Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networks. In Proceedings of the International Conference on Neuromorphic Systems, ICONS 2020 (pp. 1:1–1:8). doi:10.1145/3407197.3407225
Yin, B., Balvert, M., van der Spek, R., Dutilh, B., Bohte, S., Veldink, J., & Schönhuth, A. (2019). Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype. Bioinformatics, 35(14), i538–i547. doi:10.1093/bioinformatics/btz369
Yin, B., Balvert, M., Zambrano, D., Schönhuth, A., & Bohte, S. (2018). An image representation based convolutional network for DNA classification. In 6th International Conference on Learning Representations.