| An explainable deep machine vision framework for plant stress phenotyping S Ghosal, D Blystone, AK Singh, B Ganapathysubramanian, A Singh, ... Proceedings of the National Academy of Sciences 115 (18), 4613-4618, 2018 | 650 | 2018 |
| NTIRE 2018 Challenge on Spectral Reconstruction from RGB Images B Arad, O Ben-Shahar, R Timofte, LV Gool, L Zhang, MH Yang, Z Xiong, ... 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition …, 2018 | 315 | 2018 |
| A weakly supervised deep learning framework for sorghum head detection and counting S Ghosal, B Zheng, SC Chapman, AB Potgieter, DR Jordan, X Wang, ... Plant Phenomics, 2019 | 232 | 2019 |
| Interpretable deep learning for guided microstructure-property explorations in photovoltaics BSS Pokuri, S Ghosal, A Kokate, S Sarkar, B Ganapathysubramanian npj Computational Materials 5 (1), 95, 2019 | 76 | 2019 |
| An unsupervised spatiotemporal graphical modeling approach to anomaly detection in distributed cps C Liu, S Ghosal, Z Jiang, S Sarkar 2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS …, 2016 | 69 | 2016 |
| Deep multiview image fusion for soybean yield estimation in breeding applications LG Riera, ME Carroll, Z Zhang, JM Shook, S Ghosal, T Gao, A Singh, ... Plant Phenomics, 2021 | 67 | 2021 |
| Encoding Invariances in Deep Generative Models V Shah, A Joshi, S Ghosal, B Pokuri, S Sarkar, B Ganapathysubramanian, ... https://arxiv.org/abs/1906.01626, 2019 | 43 | 2019 |
| An unsupervised anomaly detection approach using energy-based spatiotemporal graphical modeling C Liu, S Ghosal, Z Jiang, S Sarkar Cyber-physical systems 3 (1-4), 66-102, 2017 | 43 | 2017 |
| Detection and analysis of combustion instability from hi-speed flame images using dynamic mode decomposition S Ghosal, V Ramanan, S Sarkar, SR Chakravarthy, S Sarkar Dynamic Systems and Control Conference 50695, V001T12A005, 2016 | 23 | 2016 |
| Uncertainty quantified deep learning for predicting dice coefficient of digital histopathology image segmentation S Ghosal, A Xie, P Shah arXiv preprint arXiv:2109.00115, 2021 | 12 | 2021 |
| High speed video-based health monitoring using 3D deep learning S Ghosal, A Akintayo, P Boor, S Sarkar Dynamic Data-Driven Application Systems (DDDAS), 2017 | 11 | 2017 |
| A deep-learning toolkit for visualization and interpretation of segmented medical images S Ghosal, P Shah Cell Reports Methods 1 (7), 2021 | 10 | 2021 |
| Generative models for solving nonlinear partial differential equations A Joshi, V Shah, S Ghosal, B Pokuri, S Sarkar, B Ganapathysubramanian, ... Proc. of NeurIPS Workshop on ML for Physics, 2019 | 10 | 2019 |
| Interpretable deep learning applied to plant stress phenotyping S Ghosal, D Blystone, AK Singh, B Ganapathysubramanian, A Singh, ... arXiv preprint arXiv:1710.08619, 2017 | 7 | 2017 |
| Interpretable deep learning for guided structure-property explorations in photovoltaics BSS Pokuri, S Ghosal, A Kokate, B Ganapathysubramanian, S Sarkar arXiv preprint arXiv:1811.06067, 2018 | 5 | 2018 |
| Interpretable and synergistic deep learning for visual explanation and statistical estimations of segmentation of disease features from medical images S Ghosal, P Shah arXiv preprint arXiv:2011.05791, 2020 | 4 | 2020 |
| Deep Generative Models Strike Back! Improving Understanding and Evaluation in Light of Unmet Expectations for OoD Data J Just, S Ghosal arXiv preprint arXiv:1911.04699, 2019 | 4 | 2019 |
| A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting. 2019 S Ghosal, B Zheng, SC Chapman, AB Potgieter, DR Jordan, X Wang, ... Publisher: Science Partner 2019, 2019 | 3 | 2019 |
| Binary 2d morphologies of polymer phase separation: Dataset and python toolbox V Shah, A Joshi, B Pokuri, B Ganapathysubramanian, S Ghosal, S Sarkar, ... | 2 | 2019 |
| Engineering analytics through explainable deep learning S Ghosal Iowa State University, 2017 | 2 | 2017 |
Soumik SarkarDirector, Translational AI Center, Professor, Iowa State UniversityVerified email at iastate.edu
