| Topological learning and its application to multimodal brain network integration T Songdechakraiwut, L Shen, M Chung MICCAI '21 International Conference on Medical Image Computing and Computer …, 2021 | 30 | 2021 |
| Dynamic topological data analysis for functional brain signals T Songdechakraiwut, MK Chung IEEE ISBI '20 International Symposium on Biomedical Imaging, 2020 | 24 | 2020 |
| Topological learning for brain networks T Songdechakraiwut, MK Chung Annals of Applied Statistics 17 (1), 403-433, 2023 | 22 | 2023 |
| Fast topological clustering with Wasserstein distance T Songdechakraiwut, BM Krause, MI Banks, KV Nourski, BD Van Veen ICLR '22 International Conference on Learning Representations, 2022 | 7 | 2022 |
| Wasserstein distance-preserving vector space of persistent homology T Songdechakraiwut, BM Krause, MI Banks, KV Nourski, BD Van Veen MICCAI '23 International Conference on Medical Image Computing and Computer …, 2023 | 5* | 2023 |
| Topological continual learning with Wasserstein distance and barycenter T Songdechakraiwut, X Yin, BD Van Veen NeurIPS '22 Workshop on Meta-Learning, 2022 | 1 | 2022 |
| Functional connectomes of neural networks T Songdechakraiwut, Y Wu arXiv preprint arXiv:2412.15279, 2024 | | 2024 |
| Scalable vector representation for topological data analysis based classification T Songdechakraiwut, BM Krause, MI Banks, KV Nourski, BD Van Veen NeurIPS '22 Workshop on Symmetry and Geometry in Neural Representations, 2022 | | 2022 |
| Learning to continually learn with topological regularization T Songdechakraiwut, X Yin, BD Van Veen NeurIPS '22 Workshop on Symmetry and Geometry in Neural Representations, 2022 | | 2022 |
