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Found 8 papers, 7 papers with code
Domain Generalisation via Imprecise Learning
Out-of-distribution (OOD) generalisation is challenging because it involves not only learning from empirical data, but also deciding among various notions of generalisation, e. g., optimising the average-case risk, worst-case risk, or interpolations thereof.
FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation
The result is an emulator that \textit{(i)} enjoys the flexibility of statistical machine learning models and can learn from data, and \textit{(ii)} has a robust physical grounding with interpretable parameters that can be used to make inference about the climate system.
Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge
A directed acyclic graph (DAG) provides valuable prior knowledge that is often discarded in regression tasks in machine learning.
AODisaggregation: toward global aerosol vertical profiles
In this work, we develop a framework for the vertical disaggregation of AOD into extinction profiles, i. e. the measure of light extinction throughout an atmospheric column, using readily available vertically resolved meteorological predictors such as temperature, pressure or relative humidity.
Deconditional Downscaling with Gaussian Processes
Yet, when LR samples are modeled as aggregate conditional means of HR samples with respect to a mediating variable that is globally observed, the recovery of the underlying fine-grained field can be framed as taking an "inverse" of the conditional expectation, namely a deconditioning problem.
NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations
The recent explosion in applications of machine learning to satellite imagery often rely on visible images and therefore suffer from a lack of data during the night.
Predicting Landsat Reflectance with Deep Generative Fusion
Public satellite missions are commonly bound to a trade-off between spatial and temporal resolution as no single sensor provides fine-grained acquisitions with frequent coverage.
Mixup Regularization for Region Proposal based Object Detectors
Mixup - a neural network regularization technique based on linear interpolation of labeled sample pairs - has stood out by its capacity to improve model's robustness and generalizability through a surprisingly simple formalism.
