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Found 9 papers, 6 papers with code
Improving Uncertainty-Error Correspondence in Deep Bayesian Medical Image Segmentation
Previous work has investigated the correspondence between uncertainty and error, however, no work has been done on improving the "utility" of Bayesian uncertainty maps such that it is only present in inaccurate regions and not in the accurate ones.
Accelerating hyperbolic t-SNE
The need to understand the structure of hierarchical or high-dimensional data is present in a variety of fields.
Navigating Perplexity: A linear relationship with the data set size in t-SNE embeddings
Finally, we outline several applications for the visualization of high-dimensional data via t-SNE based on this linear relationship.
Parametrizing Product Shape Manifolds by Composite Networks
Parametrizations of data manifolds in shape spaces can be computed using the rich toolbox of Riemannian geometry.
ProtoFold Neighborhood Inspector
Post-translational modifications (PTMs) affecting a protein's residues (amino acids) can disturb its function, leading to illness.
Deep vanishing point detection: Geometric priors make dataset variations vanish
Deep learning has improved vanishing point detection in images.
DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds
Learning from 3D point-cloud data has rapidly gained momentum, motivated by the success of deep learning on images and the increased availability of 3D~data.
Ranked #8 on
3D Part Segmentation
on ShapeNet-Part
Comparing Bayesian Models for Organ Contouring in Head and Neck Radiotherapy
However, in a QA context, a model should also have high uncertainty in inaccurate regions and low uncertainty in accurate regions.
CNNs on Surfaces using Rotation-Equivariant Features
We propose a network architecture for surfaces that consists of vector-valued, rotation-equivariant features.
