Sambuddha Ghosal
Sambuddha Ghosal
Verified email at mit.edu
Title
Cited by
Cited by
Year
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
1562018
Ntire 2020 challenge on spectral reconstruction from an rgb image
B Arad, R Timofte, O Ben-Shahar, YT Lin, GD Finlayson
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
542020
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
432016
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, 1525874, 2019
372019
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
342017
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
122016
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), 1-11, 2019
102019
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
72019
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
42017
An unsupervised spatiotemporal graphical modeling approach to anomaly detection in distributed cps. In 2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems …
C Liu, S Ghosal, Z Jiang, S Sarkar
IEEE, 2016
42016
Interpretable deep learning applied to plant stress phenotyping
S Ghosal, D Blystone, AK Singh, B Ganapathysubramanian, A Singh, ...
arXiv preprint arXiv:1710.08619, 2017
32017
Engineering analytics through explainable deep learning
S Ghosal
32017
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
22018
An Automated Soybean Multi-Stress Detection framework using Deep Convolutional Neural Networks
S Ghosal, D Blystone, H Saha, D Mueller, B Ganapathysubramanian, ...
2*
Data-driven persistent monitoring of Indoor Air Systems
S Ghosal, C Liu, U Passe, S He, S Sarkar
12016
Generative Models for Solving Nonlinear Partial Differential Equations
A Joshi, V Shah, S Ghosal, B Pokuri, S Sarkar, B Ganapathysubramanian, ...
1
Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications
LG Riera, ME Carroll, Z Zhang, JM Shook, S Ghosal, T Gao, A Singh, ...
arXiv preprint arXiv:2011.07118, 2020
2020
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
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
2019
Physics-constrained deterministic solution of time-dependent partial differential equations using deep convolutional encoder-decoders
S Ghosal
2019
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Articles 1–20