| Bayesian graph convolutional neural networks for semi-supervised classification Y Zhang, S Pal, M Coates, D Ustebay Proceedings of the AAAI conference on artificial intelligence 33 (01), 5829-5836, 2019 | 246 | 2019 |
| Bayesian graph convolutional neural networks using node copying S Pal, F Regol, M Coates ICML 2019 Workshop on Learning and Reasoning with Graph-Structured Data, 2019 | 21 | 2019 |
| RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting S Pal, L Ma, Y Zhang, M Coates International Conference on Machine Learning, 2021 | 20 | 2021 |
| Invertible particle-flow-based sequential MCMC with extension to Gaussian mixture noise models Y Li, S Pal, MJ Coates IEEE Transactions on Signal Processing 67 (9), 2499-2512, 2019 | 16 | 2019 |
| Bag graph: Multiple instance learning using bayesian graph neural networks S Pal, A Valkanas, F Regol, M Coates Proceedings of the AAAI Conference on Artificial Intelligence 36 (7), 7922-7930, 2022 | 15 | 2022 |
| Non parametric graph learning for Bayesian graph neural networks S Pal, S Malekmohammadi, F Regol, Y Zhang, Y Xu, M Coates Conference on uncertainty in artificial intelligence, 1318-1327, 2020 | 15 | 2020 |
| Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation F Regol, S Pal, Y Zhang, M Coates International Conference on Machine Learning, 2020 | 13 | 2020 |
| Bayesian graph convolutional neural networks using non-parametric graph learning S Pal, F Regol, M Coates ICLR 2019 Workshop on Representation Learning on Graphs and Manifolds, 2019 | 13 | 2019 |
| Particle flow particle filter for Gaussian mixture noise models S Pal, M Coates 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018 | 10 | 2018 |
| Particle flow particle filter using Gromov's method S Pal, M Coates 2019 IEEE 8th International Workshop on Computational Advances in Multi …, 2019 | 9 | 2019 |
| Scalable MCMC in degree corrected stochastic block model S Pal, M Coates IEEE International Conference on Acoustics, Speech and Signal Processing …, 2019 | 9 | 2019 |
| Detection and defense of topological adversarial attacks on graphs Y Zhang, F Regol, S Pal, S Khan, L Ma, M Coates International Conference on Artificial Intelligence and Statistics, 2989-2997, 2021 | 8 | 2021 |
| Gaussian sum particle flow filter S Pal, M Coates 2017 IEEE 7th International Workshop on Computational Advances in Multi …, 2017 | 8 | 2017 |
| Node copying: A random graph model for effective graph sampling F Regol, S Pal, J Sun, Y Zhang, Y Geng, M Coates Signal Processing 192, 108335, 2022 | 5 | 2022 |
| Sequential MCMC with the discrete bouncy particle sampler S Pal, M Coates 2018 IEEE Statistical Signal Processing Workshop (SSP), 663-667, 2018 | 5 | 2018 |
| Multi-resolution time-series transformer for long-term forecasting Y Zhang, L Ma, S Pal, Y Zhang, M Coates International Conference on Artificial Intelligence and Statistics, 4222-4230, 2024 | 3 | 2024 |
| Bayesian graph convolutional neural networks Y Zhang, S Pal, M Coates, D Ustebay US Patent 11,531,886, 2022 | 3 | 2022 |
| Node copying for protection against graph neural network topology attacks F Regol, S Pal, M Coates 2019 IEEE 8th International Workshop on Computational Advances in Multi …, 2019 | 1 | 2019 |
| Estimation of time-series on graphs using Bayesian graph convolutional neural networks F Teimury, S Pal, A Amini, M Coates Wavelets and Sparsity XVIII 11138, 298-305, 2019 | 1 | 2019 |
| CKGConv: General Graph Convolution with Continuous Kernels L Ma, S Pal, Y Zhang, J Zhou, Y Zhang, M Coates arXiv preprint arXiv:2404.13604, 2024 | | 2024 |
Mark CoatesProfessor of Electrical Engineering, McGill UniversityVerified email at ece.mcgill.ca
