Picture for Auroop R. Ganguly

Auroop R. Ganguly

Sustainability and Data Sciences Laboratory, Department of Civil and Environmental Engineering, Northeastern University, Boston, MA, USA, Pacific Northwest National Laboratory, Richland, WA, USA

CDA: Contrastive-adversarial Domain Adaptation

Jan 10, 2023
Figure 1 for CDA: Contrastive-adversarial Domain Adaptation
Figure 2 for CDA: Contrastive-adversarial Domain Adaptation
Figure 3 for CDA: Contrastive-adversarial Domain Adaptation
Figure 4 for CDA: Contrastive-adversarial Domain Adaptation
Viaarxiv icon

Robust Causality and False Attribution in Data-Driven Earth Science Discoveries

Sep 26, 2022
Figure 1 for Robust Causality and False Attribution in Data-Driven Earth Science Discoveries
Figure 2 for Robust Causality and False Attribution in Data-Driven Earth Science Discoveries
Figure 3 for Robust Causality and False Attribution in Data-Driven Earth Science Discoveries
Figure 4 for Robust Causality and False Attribution in Data-Driven Earth Science Discoveries
Viaarxiv icon

Deep Transfer Learning on Satellite Imagery Improves Air Quality Estimates in Developing Nations

Feb 17, 2022
Figure 1 for Deep Transfer Learning on Satellite Imagery Improves Air Quality Estimates in Developing Nations
Figure 2 for Deep Transfer Learning on Satellite Imagery Improves Air Quality Estimates in Developing Nations
Figure 3 for Deep Transfer Learning on Satellite Imagery Improves Air Quality Estimates in Developing Nations
Figure 4 for Deep Transfer Learning on Satellite Imagery Improves Air Quality Estimates in Developing Nations
Viaarxiv icon

Explainable deep learning for insights in El Nino and river flows

Add code
Jan 12, 2022
Figure 1 for Explainable deep learning for insights in El Nino and river flows
Figure 2 for Explainable deep learning for insights in El Nino and river flows
Figure 3 for Explainable deep learning for insights in El Nino and river flows
Figure 4 for Explainable deep learning for insights in El Nino and river flows
Viaarxiv icon

Bayesian Deep Learning Hyperparameter Search for Robust Function Mapping to Polynomials with Noise

Jun 23, 2021
Figure 1 for Bayesian Deep Learning Hyperparameter Search for Robust Function Mapping to Polynomials with Noise
Figure 2 for Bayesian Deep Learning Hyperparameter Search for Robust Function Mapping to Polynomials with Noise
Figure 3 for Bayesian Deep Learning Hyperparameter Search for Robust Function Mapping to Polynomials with Noise
Figure 4 for Bayesian Deep Learning Hyperparameter Search for Robust Function Mapping to Polynomials with Noise
Viaarxiv icon

Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling

Aug 12, 2020
Figure 1 for Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling
Figure 2 for Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling
Figure 3 for Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling
Figure 4 for Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling
Viaarxiv icon

Deep Learning Emulation of Multi-Angle Implementation of Atmospheric Correction (MAIAC)

Add code
Oct 29, 2019
Figure 1 for Deep Learning Emulation of Multi-Angle Implementation of Atmospheric Correction (MAIAC)
Figure 2 for Deep Learning Emulation of Multi-Angle Implementation of Atmospheric Correction (MAIAC)
Figure 3 for Deep Learning Emulation of Multi-Angle Implementation of Atmospheric Correction (MAIAC)
Figure 4 for Deep Learning Emulation of Multi-Angle Implementation of Atmospheric Correction (MAIAC)
Viaarxiv icon

Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning

Add code
May 24, 2018
Figure 1 for Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning
Figure 2 for Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning
Figure 3 for Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning
Figure 4 for Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning
Viaarxiv icon