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Scalable Interpolation of Satellite Altimetry Data with Probabilistic Machine Learning
In this work, we present a new open-source Python programming library for performing efficient interpolation of non-stationary …
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Scalable Interpolation of Satellite Altimetry Data with Probabilistic Machine Learning
Co-located OLCI Optical Imagery and SAR Altimetry from Sentinel-3 for Enhanced Arctic Spring Sea Ice Surface Classification
The Sentinel-3A and Sentinel-3B satellites, launched in February 2016 and April 2018 respectively, build on the legacy of CryoSat-2 by …
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Plasma Surrogate Modelling using Fourier Neural Operators
Predicting plasma evolution within a Tokamak reactor is crucial to realizing the goal of sustainable fusion. Capabilities in …
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Challenging Systematic Prejudices: An Investigation into Bias Against Women and Girls
The International Research Centre on Artificial Intelligence (IRCAI), under the auspices of UNESCO, in collaboration with UNESCO HQ, …
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Faster Training of Neural ODEs Using Gauß–Legendre Quadrature
Neural ODEs demonstrate strong performance in generative and time-series modelling. However, training them via the adjoint method is …
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Enhanced GPIS Learning Based on Local and Global Focus Areas
Implicit surface learning is one of the most widely used methods for 3D surface reconstruction from raw point cloud data. Current …
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Enhanced GPIS Learning Based on Local and Global Focus Areas
The Graph Cut Kernel for Ranked Data
Many algorithms for ranked data become computationally intractable as the number of objects grows due to the complex geometric …
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Iterative State Estimation in Non-linear Dynamical Systems Using Approximate Expectation Propagation
Bayesian inference in non-linear dynamical systems seeks to find good posterior approximations of a latent state given a sequence of …
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Iterative State Estimation in Non-linear Dynamical Systems Using Approximate Expectation Propagation
Cauchy-Schwarz Regularized Autoencoder
Recent work in unsupervised learning has focused on efficient inference and learning in latent variables models. Training these models …
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Pathwise Conditioning of Gaussian Processes
As Gaussian processes are used to answer increasingly complex questions, analytic solutions become scarcer and scarcer. Monte Carlo …
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