Ben Glocker

TL;DR: In this article, a graph convolutional Gaussian process (GCGP) is proposed to learn translation invariant relationships on non-Euclidean domains, which can be applied to problems in machine learning for which the input observations are functions with domains on general graphs.

TL;DR: A new segmentation-based score, called normalized Weighted Spectral Distance (nWSD), that measures only shape discrepancies using the spectrum of the Laplace operator is explored, which allows richer, more discriminative evaluations.

TL;DR: The experimental results indicate that the proposed framework can successfully train deep SCMs that are capable of all three levels of Pearl's ladder of causation: association, intervention, and counterfactuals, giving rise to a powerful new approach for answering causal questions in imaging applications and beyond.

TL;DR: There exists a limit to signal-to-noise below which CNNs fail to generalize and that this limit is affected by dataset size - more data leading to better performances; however, in general, higher capacity models exhibit better generalization.

TL;DR: In this paper , the authors developed an unsupervised statistical clustering model based on a mixture of probabilistic graphs for presentation and clinical, physiological, laboratory and imaging data to identify subgroups of TBI patients admitted to the intensive care unit in the CENTER-TBI dataset (N = 1,728).