15 citations · 39 across the 6 of their papers we have counts for
7 papers
VICE: Variational Interpretable Concept Embeddings
Lukas Muttenthaler, Charles Y. Zheng, Patrick McClure +3
A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces Variational Interpretable Conce…
A Deep Neural Network Tool for Automatic Segmentation of Human Body Parts in Natural Scenes
Patrick McClure, Gabrielle Reimann, Michal Ramot +1
This short article describes a deep neural network trained to perform automatic segmentation of human body parts in natural scenes. More specifically, we trained a Bayesian SegNet…
Improving the Interpretability of fMRI Decoding using Deep Neural Networks and Adversarial Robustness
Patrick McClure, Dustin Moraczewski, Ka Chun Lam +2
Deep neural networks (DNNs) are being increasingly used to make predictions from functional magnetic resonance imaging (fMRI) data. However, they are widely seen as uninterpretable…
Knowing what you know in brain segmentation using Bayesian deep neural networks
Patrick McClure, Nao Rho, John A. Lee +7
In this paper, we describe a Bayesian deep neural network (DNN) for predicting FreeSurfer segmentations of structural MRI volumes, in minutes rather than hours. The network was tra…
Distributed Weight Consolidation: A Brain Segmentation Case Study
Patrick McClure, Charles Y. Zheng, Jakub R. Kaczmarzyk +5
Collecting the large datasets needed to train deep neural networks can be very difficult, particularly for the many applications for which sharing and pooling data is complicated b…
Robustly representing uncertainty in deep neural networks through sampling
Patrick McClure, Nikolaus Kriegeskorte
As deep neural networks (DNNs) are applied to increasingly challenging problems, they will need to be able to represent their own uncertainty. Modeling uncertainty is one of the ke…