| Q. Kang*, K. Zhao*, Q. Ding, F. Ji, X. Li, W. Liang, Y. Song, and W. P. Tay, “Unleashing the potential of fractional calculus in graph neural networks with FROND,” Proc. International Conference on Learning Representations (ICLR), Vienna, Austria, May 2024, Spotlight. |
| R. She*, Q. Kang*, S. Wang*, W. P. Tay, K. Zhao, Y. Song, T. Geng, Y. Xu, D. N. Navarro, and A. Hartmannsgruber, “PointDifformer: Robust point cloud registration with neural diffusion and transformer,” IEEE Transactions on Geoscience and Remote Sensing, in press, 2024. |
| Q. Kang*, K. Zhao*, Y. Song, Y. Xie, Y. Zhao, S. Wang, R. She, and W. P. Tay, “Coupling graph neural networks with fractional order continuous dynamics: A robustness study,” Proc. AAAI Conference on Artificial Intelligence, Vancouver, Canada, Feb. 2024. |
| R. She*, S. Wang*, Q. Kang*†, K. Zhao, Y. Song, W. P. Tay, T. Geng, and X. Jian, “PosDiffNet: Positional neural diffusion for point cloud registration in a large field of view with perturbationS,” Proc. AAAI Conference on Artificial Intelligence, Vancouver, Canada, Feb. 2024. |
| S. Wang*, R. She*, Q. Kang†, X. Jian, K. Zhao, Y. Song, and W. P. Tay, “DistilVPR: Cross-modal knowledge distillation for visual place recognition,” Proc. AAAI Conference on Artificial Intelligence, Vancouver, Canada, Feb. 2024. |
| Q. Kang*†, W. P. Tay, R. She, S. Wang, X. Liu, and Y. Yang, “Multi-armed linear bandits with latent biases,” Information Sciences, Accepted, 2024. |
| Q. Kang*, K. Zhao*, Q. Ding, F. Ji, X. Li, W. Liang, Y. Song, and W. P. Tay, “Unleashing the potential of fractional calculus in graph neural networks,” NeurIPS 2023 Workshop on Machine Learning and the Physical Sciences, New Orleans, USA, Dec. 2023. |
| Q. Kang*, Y. Zhao*, K. Zhao*, X. Li, Q. Ding, W. P. Tay, and S. Wang, “Advancing graph neural networks through joint time-space dynamics,” NeurIPS 2023 Workshop on Deep Learning and Differential Equations, New Orleans, USA, Dec. 2023. |
| K. Zhao*, Q. Kang*†, Y. Song*, R. She, S. Wang, and W. P. Tay, “Adversarial robustness in graph neural networks: A Hamiltonian approach,” Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, Dec. 2023, Spotlight. |
| Q. Kang*†, K. Zhao*, Y. Song, S. Wang, and W. P. Tay, “Node embedding from neural Hamiltonian orbits in graph neural networks,” Proc. International Conference on Machine Learning (ICML), Hawaii, USA, Jul. 2023. |
| R. She*, Q. Kang*, S. Wang*, Y. Yang, K. Zhao, Y. Song, and W. P. Tay, “Robustmat: Neural diffusion for street landmark patch matching under challenging environments,” IEEE Transactions on Image Processing, early access, 2023. |
| R. She*, Q. Kang*†, S. Wang, W. P. Tay, Y. L. Guan, D. N. Navarro, and A. Hartmannsgruber, “Image patch-matching with graph-based learning in street scenes,” IEEE Transactions on Image Processing, vol. 32, pp. 3465 – 3480, Jun. 2023. |
| K. Zhao*, Q. Kang*, Y. Song, R. She, S. Wang, and W. P. Tay, “Graph neural convection-diffusion with heterophily,” Proc. International Joint Conference on Artificial Intelligence (IJCAI), Macao, China, Aug. 2023. |
| S. Wang*, Q. Kang*, R. She, W. Wang, K. Zhao, Y. Song, and W. P. Tay, “HypLiLoc: Towards effective LiDAR pose regression with hyperbolic fusion,” Proc. IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), Vancouver, Canada, Jun. 2023. |
| S. Wang*, Q. Kang*, R. She*, W. P. Tay, A. Hartmannsgruber, and D. N. Navarro, “RobustLoc: robust camera pose regression in challenging driving environments,” Proc. AAAI Conference on Artificial Intelligence, Washington, USA, Feb. 2023. |
| Y. Song*, Q. Kang*, S. Wang*, K. Zhao*, and W. P. Tay, “On the robustness of graph neural diffusion to topology perturbations,” Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, Nov. 2022. |
| Q. Kang*, Y. Song*, Q. Ding, and W. P. Tay, “Stable neural ODE with Lyapunov-stable equilibrium points for defending against adversarial attacks,” Advances in Neural Information Processing Systems (NeurIPS), virtual, Dec. 2021. |
| Y. Song*, Q. Kang*, and W. P. Tay, “Error-correcting output codes with ensemble diversity for robust learning in neural networks,” Proc. AAAI Conference on Artificial Intelligence, virtual, Feb. 2021. |
| Q. Kang and W. P. Tay, "Task recommendation in crowdsourcing based on learning preferences and reliabilities," IEEE Transactions on Services Computing, vol. 15, no. 4, pp. 1785–1798, 2022. |
| Q. Kang and W. P. Tay, "Sequential multi-class labeling in crowdsourcing," IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 11, pp. 2190 – 2199, Nov. 2019. |
| Q. Kang*, R. She*, S. Wang, W. P. Tay, N. D. Navarro, R. Khurana, and A. Hartmannsgruber, “Location learning for AVs: LiDAR and image landmarks fusion localization with graph neural networks,” in Proc. IEEE International Conference on Intelligent Transportation Systems (ITSC), Macau, China, Oct. 2022. |
| Q. Kang and W. P. Tay, “Orthogonal projection in linear bandits,” in Proc. IEEE Global Conf. on Signal and Information Processing, Ottawa, Canada, Nov. 2019. |
| Q. Kang and W. P. Tay, “Sequential multi-class labeling in crowdsourcing: A Ulam-Renyi game approach,” in IEEE/WIC/ACM Int. Conf. on Web Intelligence, Leipzig, Germany, Aug. 2017 |
| R. She*, Q. Kang*, S. Wang*, K. Zhao, Y. Song, Y. Xu, T. Geng, W. P. Tay, D. N. Navarro, and A. Hartmannsgruber, “Image patch-matching with graph-based learning in street scenes,” Proc. IEEE International Conference on Image Processing, Kuala Lumpur, Malaysia, Oct. 2023, invited paper. |