4 citations · 5 across the 6 of their papers we have counts for
7 papers
AeroMELD: A Linear Embedding of Aerosol Populations for Diagnostics and Latent Dynamics
Ehsan Saleh, Saba Ghaffari, Wenhan Tang +5
Accurately representing atmospheric aerosol populations is essential for simulating aerosol-cloud interactions, radiative forcing, and ice nucleation, yet existing reduced schemes…
Reconstructing the Aerosol State from Partial Observations with Generative Modeling
E. Saleh, S. Ghaffari, J. H. Curtis +4
Key aerosol properties that shape climate -- such as CCN activity, scattering and absorption, and ice nucleation efficiency -- are difficult to infer from measurements that typical…
Generative Modeling of Aerosol State Representations
Ehsan Saleh, Saba Ghaffari, Jeffrey H. Curtis +4
Aerosol-cloud--radiation interactions remain among the most uncertain components of the Earth's climate system, in partdue to the high dimensionality of aerosol state representatio…
Learning from Integral Losses in Physics Informed Neural Networks
Ehsan Saleh, Saba Ghaffari, Timothy Bretl +2
This work proposes a solution for the problem of training physics-informed networks under partial integro-differential equations. These equations require an infinite or a large num…
Robust Model-Based Optimization for Challenging Fitness Landscapes
Saba Ghaffari, Ehsan Saleh, Alexander G. Schwing +3
Protein design, a grand challenge of the day, involves optimization on a fitness landscape, and leading methods adopt a model-based approach where a model is trained on a training…
Truly Deterministic Policy Optimization
Ehsan Saleh, Saba Ghaffari, Timothy Bretl +1
In this paper, we present a policy gradient method that avoids exploratory noise injection and performs policy search over the deterministic landscape. By avoiding noise injection…
On the Importance of Firth Bias Reduction in Few-Shot Classification
Saba Ghaffari, Ehsan Saleh, David Forsyth +1
Learning accurate classifiers for novel categories from very few examples, known as few-shot image classification, is a challenging task in statistical machine learning and compute…