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Causality matters in medical imaging
This article discusses how the language of causality can shed new light on the major challenges in machine learning for medical imaging: 1) data scarcity, which is the limited availability of high-quality annotations, and 2) data mismatch, whereby a trained algorithm may fail to generalize in clinical practice.
Graph Convolutional Gaussian Processes
We propose a novel Bayesian nonparametric method to learn translation-invariant relationships on non-Euclidean domains.
Semi-Supervised Learning via Compact Latent Space Clustering
We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation.
An Energy Minimization Approach to 3D Non-Rigid Deformable Surface Estimation Using RGBD Data
In particular, we show that our method is able to estimate the configuration of a textureless nonrigid object with no correspondences available.
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