| Regionalization in a global hydrologic deep learning model: from physical descriptors to random vectors X Li, A Khandelwal, X Jia, K Cutler, R Ghosh, A Renganathan, S Xu, ... Water Resources Research 58 (8), e2021WR031794, 2022 | 20 | 2022 |
| Robust inverse framework using knowledge-guided self-supervised learning: An application to hydrology R Ghosh, A Renganathan, K Tayal, X Li, A Khandelwal, X Jia, C Duffy, ... Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 19* | 2022 |
| Entity aware modelling: A survey R Ghosh, H Yang, A Khandelwal, E He, A Renganathan, S Sharma, X Jia, ... arXiv preprint arXiv:2302.08406, 2023 | 5 | 2023 |
| Probabilistic inverse modeling: An application in hydrology S Sharma, R Ghosh, A Renganathan, X Li, S Chatterjee, J Nieber, C Duffy, ... Proceedings of the 2023 SIAM International Conference on Data Mining (SDM …, 2023 | 4 | 2023 |
| Koopman Invertible Autoencoder: Leveraging Forward and Backward Dynamics for Temporal Modeling K Tayal, A Renganathan, R Ghosh, X Jia, V Kumar 2023 IEEE International Conference on Data Mining (ICDM), 588-597, 2023 | 2 | 2023 |
| Towards Entity-Aware Conditional Variational Inference for Heterogeneous Time-Series Prediction: An application to Hydrology R Ghosh, A Renganathan, W McAliley, M Steinbach, C Duffy, V Kumar Proceedings of the 2024 SIAM International Conference on Data Mining (SDM …, 2024 | | 2024 |
| Message Propagation Through Time: An Algorithm for Sequence Dependency Retention in Time Series Modeling S Xu, A Khandelwal, A Renganathan, V Kumar Proceedings of the 2024 SIAM International Conference on Data Mining (SDM …, 2024 | | 2024 |
| Knowledge-guided Machine Learning for Modeling Multi-scale Processes and Data Assimilation: Streamflow Forecasting in Hydrology V Kumar, R Ghosh, ZP McEachran, A Renganathan, S Sharma, C Duffy, ... AGU23, 2023 | | 2023 |
| Task Aware Modulation using Representation Learning: An Approach for Few Shot Learning in Heterogeneous Systems A Renganathan, R Ghosh, A Khandelwal, V Kumar arXiv preprint arXiv:2310.04727, 2023 | | 2023 |
| Uncertainty Quantification in Inverse Models in Hydrology SS Chatterjee, R Ghosh, A Renganathan, X Li, S Chatterjee, J Nieber, ... arXiv preprint arXiv:2310.02193, 2023 | | 2023 |
| Uncertainty Quantification in Inverse Models in Hydrology S Sharma Chatterjee, R Ghosh, A Renganathan, X Li, S Chatterjee, ... arXiv e-prints, arXiv: 2310.02193, 2023 | | 2023 |
| Towards Entity-Aware Amortized Variational Inference: An application in Streamflow modeling R Ghosh, WA McAliley, A Renganathan, K Tayal, X Jia, V Kumar AGU Fall Meeting Abstracts 2022, H35C-06, 2022 | | 2022 |
| Effectiveness of Basin Aware Modulation in a Global Hydrologic Deep Learning Model: from Physical Descriptors to Random Vectors X Li, A Khandelwal, R Ghosh, A Renganathan, J Nieber, C Duffy, ... AGU Fall Meeting Abstracts 2021, H22G-08, 2021 | | 2021 |
Rahul GhoshApplied Scientist Amazon, PhD University of Minnesotaכתובת אימייל מאומתת בדומיין umn.edu
