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Found 5 papers, 3 papers with code
Message Passing Neural PDE Solvers
The numerical solution of partial differential equations (PDEs) is difficult, having led to a century of research so far.
Learning Likelihoods with Conditional Normalizing Flows
Normalizing Flows (NFs) are able to model complicated distributions p(y) with strong inter-dimensional correlations and high multimodality by transforming a simple base density p(z) through an invertible neural network under the change of variables formula.
Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement
Here we introduce methods to characterise different components of uncertainty in such problems and demonstrate the ideas using diffusion MRI super-resolution.
Supervised Uncertainty Quantification for Segmentation with Multiple Annotations
The accurate estimation of predictive uncertainty carries importance in medical scenarios such as lung node segmentation.
CubeNet: Equivariance to 3D Rotation and Translation
3D Convolutional Neural Networks are sensitive to transformations applied to their input.
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