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Found 5 papers, 1 papers with code
Seeing Through Clouds in Satellite Images
This paper presents a neural-network-based solution to recover pixels occluded by clouds in satellite images.
Crowd Counting with Decomposed Uncertainty
Uncertainty quantification accompanied by point estimation can lead to a more informed decision, and even improve the prediction quality.
Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion
We consider the problem of removing and replacing clouds in satellite image sequences, which has a wide range of applications in remote sensing.
QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models
In this paper, we develop a family of algorithms for optimizing superposition-structured” or “dirty” statistical estimators for high-dimensional problems involving the minimization of the sum of a smooth loss function with a hybrid regularization.
Newton-Like Methods for Sparse Inverse Covariance Estimation
The second approach, which we call the Orthant-Based Newton method, is a two-phase algorithm that first identifies an orthant face and then minimizes a smooth quadratic approximation of the objective function using the conjugate gradient method.
