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Found 16 papers, 1 papers with code
A Classification of Artificial Intelligence Systems for Mathematics Education
This chapter provides an overview of the different Artificial Intelligence (AI) systems that are being used in contemporary digital tools for Mathematics Education (ME).
Synergistic Integration of Optical and Microwave Satellite Data for Crop Yield Estimation
Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food.
Warped Gaussian Processes in Remote Sensing Parameter Estimation and Causal Inference
This paper introduces warped Gaussian processes (WGP) regression in remote sensing applications.
Consistent regression of biophysical parameters with kernel methods
This paper introduces a novel statistical regression framework that allows the incorporation of consistency constraints.
Pattern Recognition Scheme for Large-Scale Cloud Detection over Landmarks
Landmark recognition and matching is a critical step in many Image Navigation and Registration (INR) models for geostationary satellite services, as well as to maintain the geometric quality assessment (GQA) in the instrument data processing chain of Earth observation satellites.
Randomized RX for target detection
This work tackles the target detection problem through the well-known global RX method.
Nonlinear Cook distance for Anomalous Change Detection
In this work we propose a method to find anomalous changes in remote sensing images based on the chronochrome approach.
Causal Inference in Geoscience and Remote Sensing from Observational Data
Establishing causal relations between random variables from observational data is perhaps the most important challenge in today's \blue{science}.
Causal Inference in Geosciences with Kernel Sensitivity Maps
Establishing causal relations between random variables from observational data is perhaps the most important challenge in today's Science.
Randomized kernels for large scale Earth observation applications
Dealing with land cover classification of the new image sources has also turned to be a complex problem requiring large amount of memory and processing time.
Nonlinear Distribution Regression for Remote Sensing Applications
When the target variable is available at a resolution that matches the remote sensing observations, standard algorithms such as neural networks, random forests or Gaussian processes are readily available to relate the two.
Efficient Nonlinear RX Anomaly Detectors
In this letter, we propose two families of techniques to improve the efficiency of the standard kernel Reed-Xiaoli (RX) method for anomaly detection by approximating the kernel function with either {\em data-independent} random Fourier features or {\em data-dependent} basis with the Nystr\"om approach.
Living in the Physics and Machine Learning Interplay for Earth Observation
Most problems in Earth sciences aim to do inferences about the system, where accurate predictions are just a tiny part of the whole problem.
Kernel Methods and their derivatives: Concept and perspectives for the Earth system sciences
We note that model function derivatives in kernel machines is proportional to the kernel function derivative.
Fair Kernel Learning
It has been shown that not including sensitive features that bias fairness, such as gender or race, is not enough to mitigate the discrimination when other related features are included.
Sensitivity Maps of the Hilbert-Schmidt Independence Criterion
Convergence bounds of both the measure and the sensitivity map are also provided.
