26 citations · 69 across the 9 of their papers we have counts for
6 papers
Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities
Mohsen Ghafoorian, Nico Karssemeijer, Tom Heskes +7
The anatomical location of imaging features is of crucial importance for accurate diagnosis in many medical tasks. Convolutional neural networks (CNN) have had huge successes in co…
Proof Supplement - Learning Sparse Causal Models is not NP-hard (UAI2013)
Tom Claassen, Joris M. Mooij, Tom Heskes
This article contains detailed proofs and additional examples related to the UAI-2013 submission `Learning Sparse Causal Models is not NP-hard'. It describes the FCI+ algorithm: a…
Efficient sampling of Gaussian graphical models using conditional Bayes factors
Max Hinne, Alex Lenkoski, Tom Heskes +1
Bayesian estimation of Gaussian graphical models has proven to be challenging because the conjugate prior distribution on the Gaussian precision matrix, the G-Wishart distribution,…
Bounds on the Bethe Free Energy for Gaussian Networks
Botond Cseke, Tom Heskes
We address the problem of computing approximate marginals in Gaussian probabilistic models by using mean field and fractional Bethe approximations. As an extension of Welling and T…
A Logical Characterization of Constraint-Based Causal Discovery
Tom Claassen, Tom Heskes
We present a novel approach to constraint-based causal discovery, that takes the form of straightforward logical inference, applied to a list of simple, logical statements about ca…
Bayesian Inference of Whole-Brain Networks
M. Hinne, T. Heskes, M. A. J. van Gerven
In structural brain networks the connections of interest consist of white-matter fibre bundles between spatially segregated brain regions. The presence, location and orientation of…